references-2-la.bib

@article{swiderska-chadaj_learnicytes_2019,
	title = {Learnicytes in immunohistochemistry with deep learning},
	volume = {58},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841519300829},
	doi = {https://doi.org/10.1016/j.media.2019.101547},
	abstract = {The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated {CD}3+ and {CD}8+ cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (κ=0.72), whereas the average pathologists agreement with reference standard was κ=0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org.},
	pages = {101547},
	journaltitle = {Medical Image Analysis},
	author = {Swiderska-Chadaj, Zaneta and Pinckaers, Hans and Rijthoven, Mart van and Balkenhol, Maschenka and Melnikova, Margarita and Geessink, Oscar and Manson, Quirine and Sherman, Mark and Polonia, Antonio and Parry, Jeremy and Abubakar, Mustapha and Litjens, Geert and Laak, Jeroen van der and Ciompi, Francesco},
	date = {2019},
	keywords = {Computational pathology, Deep learning, Immune cell detection, Immunohistochemistry}
}

@report{marlow_haskell_2010,
	title = {Haskell 2010 Language Report},
	author = {Marlow, Simon},
	date = {2010}
}

@article{henschel_fastsurfer_2019,
	title = {{FastSurfer} – A fast and accurate deep learning based neuroimaging pipeline},
	author = {Henschel, Leonie and Conjeti, Sailesh and Estrada, Santiago and Diers, Kersten and Fischl, Bruce and Reuter, Martin},
	date = {2019},
	note = {\_eprint: 1910.03866}
}

@article{girshick_fast_2015,
	title = {Fast R-{CNN}},
	author = {Girshick, Ross},
	date = {2015},
	note = {\_eprint: 1504.08083}
}

@article{he_delving_2015,
	title = {Delving Deep into Rectifiers: Surpassing Human-Level Performance on {ImageNet} Classification},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	date = {2015},
	note = {\_eprint: 1502.01852}
}

@article{beers_deepneuro_2018,
	title = {{DeepNeuro}: an open-source deep learning toolbox for neuroimaging},
	author = {Beers, Andrew and Brown, James and Chang, Ken and Hoebel, Katharina and Gerstner, Elizabeth and Rosen, Bruce and Kalpathy-Cramer, Jayashree},
	date = {2018},
	note = {\_eprint: 1808.04589}
}

@article{zhu_deeplung_2018,
	title = {{DeepLung}: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification},
	author = {Zhu, Wentao and Liu, Chaochun and Fan, Wei and Xie, Xiaohui},
	date = {2018},
	note = {\_eprint: 1801.09555}
}

@article{devlin_bert_2018,
	title = {{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	date = {2018},
	note = {\_eprint: 1810.04805}
}

@article{he_automl_2019,
	title = {{AutoML}: A Survey of the State-of-the-Art},
	author = {He, Xin and Zhao, Kaiyong and Chu, Xiaowen},
	date = {2019},
	note = {\_eprint: 1908.00709}
}

@inproceedings{hutter_automl_2016,
	title = {{AutoML} 2016 Workshop Proceedings: Proceedings of the Workshop on Automatic Machine Learning, 24 June 2016, New York, New York, {USA}},
	volume = {64},
	series = {Proceedings of Machine Learning Research},
	publisher = {Proceedings of Machine Learning Research},
	author = {Hutter, F. and Kotthoff, L. and Vanschoren, J.},
	date = {2016}
}

@article{chen_end--end_2018,
	title = {An End-to-end Approach to Semantic Segmentation with 3D {CNN} and Posterior-{CRF} in Medical Images},
	author = {Chen, Shuai and Bruijne, Marleen de},
	date = {2018},
	note = {\_eprint: 1811.03549}
}

@article{zhang_computer_2017,
	title = {A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography},
	author = {Zhang, Jeffrey and Gajjala, Sravani and Agrawal, Pulkit and Tison, Geoffrey H. and Hallock, Laura A. and Beussink-Nelson, Lauren and Fan, Eugene and Aras, Mandar A. and Jordan, {ChaRandle} and Fleischmann, Kirsten E. and Melisko, Michelle and Qasim, Atif and Efros, Alexei and Shah, Sanjiv J. and Bajcsy, Ruzena and Deo, Rahul C.},
	date = {2017},
	note = {\_eprint: 1706.07342}
}

@inproceedings{khvostikov_3d_2018,
	title = {3D {CNN}-based classification using {sMRI} and {MD}-{DTI} images for Alzheimer disease studies},
	author = {Khvostikov, Alexander and Aderghal, Karim and Benois-Pineau, Jenny and Krylov, Andrey and Catheline, Gwenaelle},
	date = {2018},
	note = {\_eprint: 1801.05968}
}

@article{hohman_visual_2019,
	title = {Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers},
	volume = {25},
	doi = {10.1109/TVCG.2018.2843369},
	pages = {2674--2693},
	number = {8},
	journaltitle = {{IEEE} Transactions on Visualization and Computer Graphics},
	author = {Hohman, F. and Kahng, M. and Pienta, R. and Chau, D. H.},
	date = {2019},
	keywords = {Computational modeling, Conferences, Data visualization, Deep learning, Machine learning, Neural networks, Visual analytics, information visualization, neural networks, visual analytics}
}

@inproceedings{mendoza_towards_2016,
	title = {Towards Automatically-Tuned Neural Networks},
	booktitle = {{AutoML}@{ICML}},
	author = {Mendoza, Hector and Klein, Aaron and Feurer, Matthias and Springenberg, Jost Tobias and Hutter, Frank},
	date = {2016}
}

@article{zela_towards_2018,
	title = {Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search},
	volume = {abs/1807.06906},
	journaltitle = {{ArXiv}},
	author = {Zela, Arber and Klein, Aaron and Falkner, Stefan and Hutter, Frank},
	date = {2018}
}

@article{jamaludin_spinenet_2017,
	title = {{SpineNet}: Automated classification and evidence visualization in spinal {MRIs}},
	volume = {41},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S136184151730110X},
	doi = {https://doi.org/10.1016/j.media.2017.07.002},
	abstract = {The objective of this work is to automatically produce radiological gradings of spinal lumbar {MRIs} and also localize the predicted pathologies. We show that this can be achieved via a Convolutional Neural Network ({CNN}) framework that takes intervertebral disc volumes as inputs and is trained only on disc-specific class labels. Our contributions are: (i) a {CNN} architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii) showing that this architecture can be trained using a multi-task loss function without requiring segmentation level annotation; and (iii) a localization method that clearly shows pathological regions in the disc volumes. We compare three visualization methods for the localization. The network is applied to a large corpus of {MRI} T2 sagittal spinal {MRIs} (using a standard clinical scan protocol) acquired from multiple machines, and is used to automatically compute disk and vertebra gradings for each {MRI}. These are: Pfirrmann grading, disc narrowing, upper/lower endplate defects, upper/lower marrow changes, spondylolisthesis, and central canal stenosis. We report near human performances across the eight gradings, and also visualize the evidence for these gradings localized on the original scans.},
	pages = {63 -- 73},
	journaltitle = {Medical Image Analysis},
	author = {Jamaludin, Amir and Kadir, Timor and Zisserman, Andrew},
	date = {2017},
	keywords = {{MRI} analysis, Radiological classification, Spinal {MRI}}
}

@article{hutter_sequential_2011,
	title = {Sequential model-based optimization for general algorithm configuration},
	pages = {507--523},
	author = {Hutter, Frank and Hoos, Holger H and Leytonbrown, Kevin},
	date = {2011}
}

@article{brock_smash_2017,
	title = {{SMASH}: One-Shot Model Architecture Search through {HyperNetworks}},
	volume = {abs/1708.05344},
	journaltitle = {{ArXiv}},
	author = {Brock, Andrew and Lim, Theodore and Ritchie, James M. and Weston, Nick},
	date = {2017}
}

@inproceedings{real_regularized_2018,
	title = {Regularized Evolution for Image Classifier Architecture Search},
	booktitle = {{AAAI}},
	author = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V.},
	date = {2018}
}

@article{ruifrok_quantification_2001,
	title = {Quantification of histochemical staining by color deconvolution},
	volume = {23},
	pages = {291--299},
	number = {4},
	journaltitle = {Analytical and Quantitative Cytology and Histology},
	author = {Ruifrok, Arnout C C and Johnston, Dennis A},
	date = {2001}
}

@article{bergstra_random_2012,
	title = {Random search for hyper-parameter optimization},
	volume = {13},
	pages = {281--305},
	number = {1},
	journaltitle = {Journal of Machine Learning Research},
	author = {Bergstra, James and Bengio, Yoshua},
	date = {2012}
}

@article{liu_progressive_2017,
	title = {Progressive Neural Architecture Search},
	volume = {abs/1712.00559},
	journaltitle = {{ArXiv}},
	author = {Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li-Jia and Fei-Fei, Li and Yuille, Alan and Huang, Jonathan and Murphy, Kevin L.},
	date = {2017}
}

@article{snoek_practical_2012,
	title = {Practical Bayesian Optimization of Machine Learning Algorithms},
	pages = {2951--2959},
	author = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P},
	date = {2012}
}

@article{zhong_practical_2017,
	title = {Practical Block-Wise Neural Network Architecture Generation},
	pages = {2423--2432},
	journaltitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition},
	author = {Zhong, Zhao and Yan, Junjie and Wu, Wei and Shao, Jing and Liu, Cheng-Lin},
	date = {2017}
}

@article{wilhelms_octrees_1992,
	title = {Octrees for Faster Isosurface Generation},
	volume = {11},
	issn = {0730-0301},
	url = {http://doi.acm.org/10.1145/130881.130882},
	doi = {10.1145/130881.130882},
	pages = {201--227},
	number = {3},
	journaltitle = {{ACM} Trans. Graph.},
	author = {Wilhelms, Jane and Van Gelder, Allen},
	date = {1992},
	note = {Place: New York, {NY}, {USA}
Publisher: {ACM}},
	keywords = {hierarchical spatial enumeration, isosurface extraction, octree, scientific visualization}
}

@inproceedings{wei_network_2016,
	title = {Network Morphism},
	booktitle = {{ICML}},
	author = {Wei, Tao and Wang, Changhu and Rui, Yong and Chen, Chang Wen},
	date = {2016}
}

@article{zoph_neural_2016,
	title = {Neural Architecture Search with Reinforcement Learning},
	volume = {abs/1611.01578},
	journaltitle = {{ArXiv}},
	author = {Zoph, Barret and Le, Quoc V.},
	date = {2016}
}

@article{chen_net2net_2015,
	title = {Net2Net: Accelerating Learning via Knowledge Transfer},
	volume = {abs/1511.05641},
	journaltitle = {{CoRR}},
	author = {Chen, Tianqi and Goodfellow, Ian J. and Shlens, Jonathon},
	date = {2015}
}

@article{zoph_learning_2017,
	title = {Learning Transferable Architectures for Scalable Image Recognition},
	pages = {8697--8710},
	journaltitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition},
	author = {Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V.},
	date = {2017}
}

@inproceedings{real_large-scale_2017,
	title = {Large-Scale Evolution of Image Classifiers},
	booktitle = {{ICML}},
	author = {Real, Esteban and Moore, Sherry and Selle, Andrew and Saxena, Saurabh and Suematsu, Yutaka Leon and Tan, Jie and Le, Quoc V. and Kurakin, Alexey},
	date = {2017}
}

@article{armeni_joint_2017,
	title = {Joint 2D-3D-Semantic Data for Indoor Scene Understanding},
	volume = {abs/1702.01105},
	url = {http://arxiv.org/abs/1702.01105},
	journaltitle = {{CoRR}},
	author = {Armeni, Iro and Sax, Sasha and Zamir, Amir Roshan and Savarese, Silvio},
	date = {2017},
	note = {\_eprint: 1702.01105},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics}
}

@inproceedings{silberman_indoor_2012,
	location = {Berlin, Heidelberg},
	title = {Indoor Segmentation and Support Inference from {RGBD} Images},
	isbn = {978-3-642-33714-7},
	url = {http://dx.doi.org/10.1007/978-3-642-33715-4_54},
	doi = {10.1007/978-3-642-33715-4_54},
	series = {{ECCV}'12},
	pages = {746--760},
	booktitle = {Proceedings of the 12th European Conference on Computer Vision - Volume Part V},
	publisher = {Springer-Verlag},
	author = {Silberman, Nathan and Hoiem, Derek and Kohli, Pushmeet and Fergus, Rob},
	date = {2012},
	note = {event-place: Florence, Italy}
}

@article{ren_faster_2015,
	title = {Faster R-{CNN}: Towards Real-Time Object Detection with Region Proposal Networks},
	volume = {abs/1506.01497},
	url = {http://arxiv.org/abs/1506.01497},
	journaltitle = {{CoRR}},
	author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross B. and Sun, Jian},
	date = {2015},
	note = {\_eprint: 1506.01497}
}

@article{liu_hierarchical_2017,
	title = {Hierarchical Representations for Efficient Architecture Search},
	volume = {abs/1711.00436},
	journaltitle = {{ArXiv}},
	author = {Liu, Hanxiao and Simonyan, Karen and Vinyals, Oriol and Fernando, Chrisantha and Kavukcuoglu, Koray},
	date = {2017}
}

@article{klein_fast_2016,
	title = {Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets},
	volume = {abs/1605.07079},
	journaltitle = {{ArXiv}},
	author = {Klein, Aaron and Falkner, Stefan and Bartels, Simon and Hennig, Philipp and Hutter, Frank},
	date = {2016}
}

@article{stanley_evolving_2001,
	title = {Evolving Neural Networks through Augmenting Topologies},
	volume = {10},
	pages = {99--127},
	journaltitle = {Evolutionary Computation},
	author = {Stanley, Kenneth O. and Miikkulainen, Risto},
	date = {2001}
}

@article{klokov_escape_2017,
	title = {Escape from Cells: Deep Kd-Networks for The Recognition of 3D Point Cloud Models},
	volume = {abs/1704.01222},
	url = {http://arxiv.org/abs/1704.01222},
	journaltitle = {{CoRR}},
	author = {Klokov, Roman and Lempitsky, Victor S.},
	date = {2017},
	note = {\_eprint: 1704.01222}
}

@article{pham_efficient_2018,
	title = {Efficient Neural Architecture Search via Parameter Sharing},
	volume = {abs/1802.03268},
	journaltitle = {{ArXiv}},
	author = {Pham, Hieu and Guan, Melody Y. and Zoph, Barret and Le, Quoc V. and Dean, Jeff},
	date = {2018}
}

@inproceedings{cai_efficient_2017,
	title = {Efficient Architecture Search by Network Transformation},
	booktitle = {{AAAI}},
	author = {Cai, Han and Chen, Tianyao and Zhang, Weinan and Yu, Yong and Wang, Jun},
	date = {2017}
}

@article{baker_designing_2016,
	title = {Designing Neural Network Architectures using Reinforcement Learning},
	volume = {abs/1611.02167},
	journaltitle = {{ArXiv}},
	author = {Baker, Bowen and Gupta, Otkrist and Naik, Nikhil and Raskar, Ramesh},
	date = {2016}
}

@article{liu_darts_2018,
	title = {{DARTS}: Differentiable Architecture Search},
	volume = {abs/1806.09055},
	journaltitle = {{ArXiv}},
	author = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
	date = {2018}
}

@inproceedings{jin_auto-keras_2018,
	title = {Auto-Keras: An Efficient Neural Architecture Search System},
	booktitle = {{KDD}},
	author = {Jin, Haifeng and Song, Qingquan and Hu, Xia},
	date = {2018}
}

@article{mcculloch_logical_1943,
	title = {A logical calculus of the ideas immanent in nervous activity},
	volume = {5},
	issn = {1522-9602},
	url = {https://doi.org/10.1007/BF02478259},
	doi = {10.1007/BF02478259},
	abstract = {Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.},
	pages = {115--133},
	number = {4},
	journaltitle = {The bulletin of mathematical biophysics},
	author = {{McCulloch}, Warren S. and Pitts, Walter},
	date = {1943}
}

@article{esteves_3d_2017,
	title = {3D object classification and retrieval with Spherical {CNNs}},
	volume = {abs/1711.06721},
	url = {http://arxiv.org/abs/1711.06721},
	journaltitle = {{CoRR}},
	author = {Esteves, Carlos and Allen-Blanchette, Christine and Makadia, Ameesh and Daniilidis, Kostas},
	date = {2017},
	note = {\_eprint: 1711.06721}
}

@book{zhang_visual_2018,
	title = {Visual Interpretability for Deep Learning: a Survey},
	author = {Zhang, Quanshi and Zhu, Song-Chun},
	date = {2018},
	note = {\_eprint: 1802.00614}
}

@article{yong_survey_2012,
	title = {A Survey of Visualization Tools in Medical Imaging},
	volume = {56},
	issn = {1877-0428},
	url = {http://www.sciencedirect.com/science/article/pii/S187704281204116X},
	doi = {https://doi.org/10.1016/j.sbspro.2012.09.654},
	abstract = {More than 30 students from university campus participated in the Development of Biomedical Image Processing Software Package for New Learners Survey investigating the use of software package for processing and editing image. The survey was available online for six months. Facts and opinions were sought to learn the general information, interactive image processing tool, non-interactive (automatic) tool, current status and future of image processing package tool. Composed of 19 questions, the survey built a comprehensive picture of the software package, programming language, workflow of the tool and captured the attitudes of the respondents. Result shows that {MATLAB} was difficult to use but it was viewed in high regard however. The result of this study is expected to be beneficial and able to assist users on effective image processing and analysis in a newly developed software package.},
	pages = {265 -- 271},
	journaltitle = {Procedia - Social and Behavioral Sciences},
	author = {Yong, Ching Yee and Chew, Kim Mey and Mahmood, Nasrul Humaimi and Ariffin, Ismail},
	date = {2012},
	keywords = {Image editting, Image processing, Medical imaging, Software package, Visualisation tools}
}

@book{muller_miscnn_2019,
	title = {{MIScnn}: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
	author = {Müller, Dominik and Kramer, Frank},
	date = {2019},
	note = {\_eprint: 1910.09308}
}

@article{chetlur_cudnn_2014,
	title = {{cuDNN}: Efficient Primitives for Deep Learning},
	journaltitle = {{arXiv}: Neural and Evolutionary Computing},
	author = {Chetlur, Sharan and Woolley, Cliff and Vandermersch, Philippe and Cohen, Jonathan D and Tran, John and Catanzaro, Bryan and Shelhamer, Evan},
	date = {2014}
}

@article{maloney_scratch_2010,
	title = {The Scratch Programming Language and Environment},
	volume = {10},
	doi = {10.1145/1868358.1868363},
	pages = {16},
	journaltitle = {{ACM} Transactions on Computing Education ({TOCE})},
	author = {Maloney, John and Resnick, Mitchel and Rusk, Natalie and Silverman, Brian and Eastmond, Evelyn},
	date = {2010}
}

@article{fischl_freesurfer_2012,
	title = {{FreeSurfer}},
	volume = {62},
	issn = {1095-9572 (Electronic) 1053-8119 (Linking)},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3685476/},
	doi = {10.1016/j.neuroimage.2012.01.021},
	abstract = {{FreeSurfer} is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.},
	pages = {774--81},
	number = {2},
	journaltitle = {Neuroimage},
	author = {Fischl, B.},
	date = {2012},
	keywords = {*Algorithms, 20th Century, 21st Century, Brain Mapping/*history/methods, Brain/anatomy \& histology, Computer-Assisted/*history/methods, History, Humans, Image Processing, Magnetic Resonance Imaging/*history/methods, Software/*history}
}

@article{walt_numpy_2011,
	title = {The {NumPy} Array: A Structure for Efficient Numerical Computation},
	volume = {13},
	doi = {10.1109/MCSE.2011.37},
	pages = {22--30},
	number = {2},
	journaltitle = {Computing in Science Engineering},
	author = {Walt, S. van der and Colbert, S. C. and Varoquaux, G.},
	date = {2011-03},
	keywords = {Arrays, Computational efficiency, Finite element methods, {NumPy}, Numerical analysis, Performance evaluation, Python, Python programming language, Resource management, Vector quantization, data structures, high level language, high level languages, mathematics computing, numerical analysis, numerical computation, numerical computations, numerical data, numpy array, programming libraries, scientific programming}
}

@article{paszke_automatic_2017,
	title = {Automatic differentiation in {PyTorch}},
	author = {Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and {DeVito}, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
	date = {2017}
}

@article{lowekamp_design_2013,
	title = {The design of {simpleITK}},
	volume = {7},
	doi = {10.3389/fninf.2013.00045},
	pages = {45},
	journaltitle = {Frontiers in neuroinformatics},
	author = {Lowekamp, Bradley and Chen, David and Ibanez, Luis and Blezek, Daniel},
	date = {2013}
}

@article{oliphant_python_2007,
	title = {Python for Scientific Computing},
	volume = {9},
	url = {https://aip.scitation.org/doi/abs/10.1109/MCSE.2007.58},
	doi = {10.1109/MCSE.2007.58},
	pages = {10--20},
	number = {3},
	journaltitle = {Computing in Science \& Engineering},
	author = {Oliphant, Travis E.},
	date = {2007},
	note = {\_eprint: https://aip.scitation.org/doi/pdf/10.1109/{MCSE}.2007.58}
}

@article{jenkinson_fsl_2012,
	title = {{FSL}},
	volume = {62},
	issn = {1053-8119},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811911010603},
	doi = {https://doi.org/10.1016/j.neuroimage.2011.09.015},
	abstract = {{FSL} (the {FMRIB} Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion {MRI} brain imaging data, written mainly by members of the Analysis Group, {FMRIB}, Oxford. For this {NeuroImage} special issue on “20 years of {fMRI}” we have been asked to write about the history, developments and current status of {FSL}. We also include some descriptions of parts of {FSL} that are not well covered in the existing literature. We hope that some of this content might be of interest to users of {FSL}, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.},
	pages = {782 -- 790},
	number = {2},
	journaltitle = {{NeuroImage}},
	author = {Jenkinson, Mark and Beckmann, Christian F. and Behrens, Timothy E. J. and Woolrich, Mark W. and Smith, Stephen M.},
	date = {2012},
	keywords = {{FSL}, Software}
}

@article{avants_reproducible_2011,
	title = {A reproducible evaluation of {ANTs} similarity metric performance in brain image registration},
	volume = {54},
	issn = {1053-8119},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811910012061},
	doi = {https://doi.org/10.1016/j.neuroimage.2010.09.025},
	abstract = {The United States National Institutes of Health ({NIH}) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools ({ANTs}), which is approaching its 2.0 release. The {ANTs} open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use {ANTs} to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the {ANTs} implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted {MRI} brain image data of 40 subjects ({UCLA}'s {LPBA}40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best {ANTs} methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.},
	pages = {2033 -- 2044},
	number = {3},
	journaltitle = {{NeuroImage}},
	author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.},
	date = {2011}
}

@book{ibanez_itk_2003,
	edition = {First},
	title = {The {ITK} Software Guide},
	publisher = {Kitware, Inc.},
	author = {Ibanez, L. and Schroeder, W. and Ng, L. and Cates, J.},
	date = {2003}
}

@article{steiner_pytorch_2019,
	title = {{PyTorch}: An Imperative Style, High-Performance Deep Learning Library},
	author = {Steiner, Benoit and Devito, Zachary and Chintala, Soumith and Gross, Sam and Paszke, Adam and Massa, Francisco and Lerer, Adam and Chanan, Gregory and Lin, Zeming and Yang, Edward and {others}},
	date = {2019}
}

@article{goode_openslide_2013,
	title = {{OpenSlide}: A vendor-neutral software foundation for digital pathology},
	volume = {4},
	pages = {27--27},
	number = {1},
	journaltitle = {Journal of Pathology Informatics},
	author = {Goode, Adam and Gilbert, Benjamin and Harkes, Jan and Jukic, Drazen M and Satyanarayanan, Mahadev},
	date = {2013}
}

@article{gibson_niftynet_2018,
	title = {{NiftyNet}: a deep-learning platform for medical imaging},
	volume = {158},
	issn = {0169-2607},
	url = {http://www.sciencedirect.com/science/article/pii/S0169260717311823},
	doi = {https://doi.org/10.1016/j.cmpb.2018.01.025},
	abstract = {Background and objectives Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source {NiftyNet} platform for deep learning in medical imaging. The ambition of {NiftyNet} is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. Methods The {NiftyNet} infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the {NiftyNet} pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. {NiftyNet} is built on the {TensorFlow} framework and supports features such as {TensorBoard} visualization of 2D and 3D images and computational graphs by default. Results We present three illustrative medical image analysis applications built using {NiftyNet} infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. Conclusions The {NiftyNet} infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.},
	pages = {113 -- 122},
	journaltitle = {Computer Methods and Programs in Biomedicine},
	author = {Gibson, Eli and Li, Wenqi and Sudre, Carole and Fidon, Lucas and Shakir, Dzhoshkun I. and Wang, Guotai and Eaton-Rosen, Zach and Gray, Robert and Doel, Tom and Hu, Yipeng and Whyntie, Tom and Nachev, Parashkev and Modat, Marc and Barratt, Dean C. and Ourselin, Sébastien and Cardoso, M. Jorge and Vercauteren, Tom},
	date = {2018},
	keywords = {Convolutional neural network, Deep learning, Generative adversarial network, Image regression, Medical image analysis, Segmentation}
}

@article{abadi_tensorflow_2016,
	title = {{TensorFlow}: A system for large-scale machine learning},
	volume = {abs/1605.08695},
	url = {http://arxiv.org/abs/1605.08695},
	journaltitle = {{CoRR}},
	author = {Abadi, Martín and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and Kudlur, Manjunath and Levenberg, Josh and Monga, Rajat and Moore, Sherry and Murray, Derek Gordon and Steiner, Benoit and Tucker, Paul A. and Vasudevan, Vijay and Warden, Pete and Wicke, Martin and Yu, Yuan and Zhang, Xiaoqiang},
	date = {2016},
	note = {\_eprint: 1605.08695}
}

@article{jia_caffe_2014,
	title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
	journaltitle = {{arXiv} preprint {arXiv}:1408.5093},
	author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
	date = {2014}
}

@article{magee_colour_2009,
	title = {Colour Normalisation in Digital Histopathology Images},
	journaltitle = {Proc Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy ({MICCAI} Workshop)},
	author = {Magee, Derek and Treanor, Darren and Crellin, Doreen and Shires, Michael and Smith, Katherine and Mohee, Kevin and Quirke, Philip},
	date = {2009}
}

@article{vahadane_structure-preserving_2016,
	title = {Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images},
	volume = {35},
	pages = {1962--1971},
	number = {8},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Vahadane, Abhishek and Peng, Tingying and Sethi, Amit and Albarqouni, Shadi and Wang, Lichao and Baust, Maximilian and Steiger, Katja and Schlitter, Anna Melissa and Esposito, Irene and Navab, Nassir},
	date = {2016}
}

@article{reinhard_color_2001,
	title = {Color Transfer between Images},
	volume = {21},
	doi = {10.1109/38.946629},
	pages = {34--41},
	journaltitle = {{IEEE} Computer Graphics and Applications},
	author = {Reinhard, Erik and Ashikhmin, Michael and Gooch, Bruce and Shirley, Peter},
	date = {2001}
}

@article{srameshkumar_speckle_2016,
	title = {Speckle Noise Removal in {MRI} Scan Image Using {WB} – Filter},
	volume = {5},
	issn = {2319-8753},
	number = {12},
	journaltitle = {International Journal of Innovative Research in Science Engineering and Technology},
	author = {{S.Rameshkumar} and Thilak, J. Anish Jafrin and {Dr.P.Suresh} and {S.Sathishkumar} and {N.Subramani}},
	date = {2016-12}
}

@article{ssenthilraja_noise_2014,
	title = {Noise Reduction in Computed Tomography Image Using {WB} – Filter},
	volume = {5},
	issn = {2229-5518},
	number = {3},
	journaltitle = {International Journal of Scientific \& Engineering Research},
	author = {{S.Senthilraja} and {Dr.P.Suresh} and {Dr.M.Suganthi}},
	date = {2014-03}
}

@article{tustison_n4itk_2010,
	title = {N4ITK: Improved N3 Bias Correction},
	volume = {29},
	doi = {10.1109/TMI.2010.2046908},
	pages = {1310--1320},
	number = {6},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
	date = {2010-06},
	keywords = {Algorithms, Approximation algorithms, Artifacts, Availability, B-spline approximation, B-spline least-squares fitting, Brain, Brain modeling, Computer-Assisted, Documentation, Humans, Image Enhancement, Image Interpretation, Image databases, Lungs, Magnetic Resonance Imaging, N3, N4ITK, Reproducibility of Results, Robustness, Sensitivity and Specificity, Spline, Testing, bias correction, bias field, biomedical {MRI}, brain, hierarchical optimization scheme, image analysis, image segmentation, inhomogeneity, lung, lung image data, medical image processing, nonparametric nonuniform intensity normalization}
}

@book{ferdouse_simulation_2011,
	title = {Simulation and Performance Analysis of Adaptive Filtering Algorithms in Noise Cancellation},
	author = {Ferdouse, Lilatul and Akhter, Nasrin and Nipa, Tamanna Haque and Jaigirdar, Fariha Tasmin},
	date = {2011},
	note = {\_eprint: 1104.1962}
}

@incollection{ogiela_preprocessing_2008,
	location = {Berlin, Heidelberg},
	title = {Preprocessing medical images and their overall enhancement},
	isbn = {978-3-540-75402-2},
	url = {https://doi.org/10.1007/978-3-540-75402-2_4},
	abstract = {This chapter briefly discusses the main stages of image preprocessing. The introduction to this book mentioned that the preprocessing of medical image is subject to certain restrictions and is generally more complex than the processing of other image types [26, 52]. This is why, of the many different techniques and methods for image filtering, we have decided to discuss here only selected ones, most frequently applied to medical images and which have been proven to be suitable for that purpose in numerous practical cases. Their operation will be illustrated with examples of simple procedures aimed at improving the quality of imaging and allowing significant information to be generated for its use at the stages of image interpretation.},
	pages = {65--97},
	booktitle = {Modern Computational Intelligence Methods for the Interpretation of Medical Images},
	publisher = {Springer Berlin Heidelberg},
	author = {Ogiela, Marek R. and Tadeusiewicz, Ryszard},
	date = {2008},
	doi = {10.1007/978-3-540-75402-2_4}
}

@article{jeyavathana_survey_2016,
	title = {A Survey: Analysis on Pre-processing and Segmentation Techniques for Medical Images},
	journaltitle = {International Journal of Research and Scientific Innovation ({IJRSI})},
	author = {Jeyavathana, R and Ramasamy, Balasubramanian and Pandian, Anbarasa},
	date = {2016}
}

@article{yao_survey_2017,
	title = {A Survey on Pre-Processing in Image Matting},
	volume = {32},
	issn = {1860-4749},
	url = {https://doi.org/10.1007/s11390-017-1709-z},
	doi = {10.1007/s11390-017-1709-z},
	abstract = {Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask (Trimap). This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories: static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.},
	pages = {122--138},
	number = {1},
	journaltitle = {Journal of Computer Science and Technology},
	author = {Yao, Gui-Lin},
	date = {2017}
}

@article{radul_functional_2001,
	title = {Functional Representations of Lawson Monads},
	volume = {9},
	pages = {457--463},
	journaltitle = {Applied Categorical Structures},
	author = {Radul, Taras},
	date = {2001}
}

@inproceedings{lee_survey_2015,
	title = {A survey of medical image processing tools},
	doi = {10.1109/ICSECS.2015.7333105},
	pages = {171--176},
	booktitle = {2015 4th International Conference on Software Engineering and Computer Systems ({ICSECS})},
	author = {Lee, L. and Liew, S.},
	date = {2015},
	keywords = {Biomedical image processing, Image segmentation, Medical diagnostic imaging, Software, clinical study, computer vision, diagnostic radiography, graphical schematic diagram, image processing, medical image processing, medical image processing software tool, medical image processing tools, operating systems, pipelined processors, radiation therapy, radiographic techniques, radiotherapy preparation, software tools, tools component, treatment planning}
}

@book{crankshaw_inferline_2018,
	title = {{InferLine}: {ML} Inference Pipeline Composition Framework},
	author = {Crankshaw, Daniel and Sela, Gur-Eyal and Zumar, Corey and Mo, Xiangxi and Gonzalez, Joseph E. and Stoica, Ion and Tumanov, Alexey},
	date = {2018},
	note = {\_eprint: 1812.01776}
}

@book{rajchl_neuronet_2018,
	title = {{NeuroNet}: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines},
	author = {Rajchl, Martin and Pawlowski, Nick and Rueckert, Daniel and Matthews, Paul M. and Glocker, Ben},
	date = {2018},
	note = {\_eprint: 1806.04224}
}

@book{rajan_pi-pe_2019,
	title = {Pi-{PE}: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D {CT} Images},
	author = {Rajan, Deepta and Beymer, David and Abedin, Shafiqul and Dehghan, Ehsan},
	date = {2019},
	note = {\_eprint: 1910.02175}
}

@book{zhang_leveraging_2019,
	title = {Leveraging Vision Reconstruction Pipelines for Satellite Imagery},
	author = {Zhang, Kai and Sun, Jin and Snavely, Noah},
	date = {2019},
	note = {\_eprint: 1910.02989}
}

@book{skibbe_marmonet_2019,
	title = {{MarmoNet}: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography},
	author = {Skibbe, Henrik and Watakabe, Akiya and Nakae, Ken and Gutierrez, Carlos Enrique and Tsukada, Hiromichi and Hata, Junichi and Kawase, Takashi and Gong, Rui and Woodward, Alexander and Doya, Kenji and Okano, Hideyuki and Yamamori, Tetsuo and Ishii, Shin},
	date = {2019},
	note = {\_eprint: 1908.00876}
}

@book{yang_xlnet_2019,
	title = {{XLNet}: Generalized Autoregressive Pretraining for Language Understanding},
	author = {Yang, Zhilin and Dai, Zihang and Yang, Yiming and Carbonell, Jaime and Salakhutdinov, Ruslan and Le, Quoc V.},
	date = {2019},
	note = {\_eprint: 1906.08237}
}

@article{chen_dual_2017,
	title = {Dual Path Networks},
	journaltitle = {{arXiv}: Computer Vision and Pattern Recognition},
	author = {Chen, Yunpeng and Li, Jianan and Xiao, Huaxin and Jin, Xiaojie and Yan, Shuicheng and Feng, Jiashi},
	date = {2017}
}

@article{he_mask_2017,
	title = {Mask R-{CNN}},
	journaltitle = {{arXiv}: Computer Vision and Pattern Recognition},
	author = {He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
	date = {2017}
}

@book{simonyan_very_2014,
	title = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
	author = {Simonyan, Karen and Zisserman, Andrew},
	date = {2014},
	note = {\_eprint: 1409.1556}
}

@inproceedings{trullo_segmentation_2017,
	title = {Segmentation of Organs at Risk in thoracic {CT} images using a {SharpMask} architecture and Conditional Random Fields},
	volume = {2017},
	doi = {10.1109/ISBI.2017.7950685},
	pages = {1003--1006},
	booktitle = {Proceedings. {IEEE} International Symposium on Biomedical Imaging},
	author = {Trullo, R. and Petitjean, Caroline and Ruan, Su and Dubray, Bernard and Nie, D. and Shen, D.},
	date = {2017}
}

@book{berman_lovasz-softmax_2017,
	title = {The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks},
	author = {Berman, Maxim and Triki, Amal Rannen and Blaschko, Matthew B.},
	date = {2017},
	note = {\_eprint: 1705.08790}
}

@book{milletari_v-net_2016,
	title = {V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation},
	author = {Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad},
	date = {2016},
	note = {\_eprint: 1606.04797}
}

@incollection{rumelhart_neurocomputing_1988,
	location = {Cambridge, {MA}, {USA}},
	title = {Neurocomputing: Foundations of Research},
	isbn = {0-262-01097-6},
	url = {http://dl.acm.org/citation.cfm?id=65669.104451},
	pages = {696--699},
	publisher = {{MIT} Press},
	author = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
	editor = {Anderson, James A. and Rosenfeld, Edward},
	date = {1988},
	note = {Section: Learning Representations by Back-propagating Errors}
}

@article{werbos_beyond_1974,
	title = {Beyond regression : new tools for prediction and analysis in the behavioral sciences /},
	author = {Werbos, Paul and J. (Paul John, Paul},
	date = {1974}
}

@article{ruder_overview_2016,
	title = {An overview of gradient descent optimization algorithms},
	volume = {abs/1609.04747},
	url = {http://arxiv.org/abs/1609.04747},
	journaltitle = {{CoRR}},
	author = {Ruder, Sebastian},
	date = {2016},
	note = {\_eprint: 1609.04747}
}

@article{ioffe_batch_2015,
	title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
	volume = {abs/1502.03167},
	url = {http://arxiv.org/abs/1502.03167},
	journaltitle = {{CoRR}},
	author = {Ioffe, Sergey and Szegedy, Christian},
	date = {2015},
	note = {\_eprint: 1502.03167}
}

@article{srivastava_dropout_2014,
	title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
	volume = {15},
	url = {http://jmlr.org/papers/v15/srivastava14a.html},
	pages = {1929--1958},
	journaltitle = {Journal of Machine Learning Research},
	author = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
	date = {2014}
}

@online{noauthor_stochastic_2017,
	title = {Stochastic gradient descent},
	url = {https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Extensions_and_variants},
	date = {2017-08}
}

@incollection{bottou_stochastic_2012,
	title = {Stochastic Gradient Descent Tricks},
	url = {https://doi.org/10.1007/978-3-642-35289-8_25},
	pages = {421--436},
	booktitle = {Neural Networks: Tricks of the Trade - Second Edition},
	author = {Bottou, Léon},
	date = {2012},
	doi = {10.1007/978-3-642-35289-8_25}
}

@unpublished{zhang_unknow_nodate,
	title = {unknow},
	author = {Zhang, Liang and Kong, Xiangwen}
}

@unpublished{zhang_block_nodate,
	title = {Block Level Skip Connections across Cascaded V-Net for Multi-organ Segmentation {\textbackslash}Huge todo, and this paper is under publishing},
	author = {Zhang, Liang and Zhang, Jiaming}
}

@unpublished{zhang_u-net_nodate,
	title = {U-net based analysis of {MRI} for Alzheimer’s disease diagnosis {\textbackslash}Huge todo, and this paper is under publishing},
	author = {Zhang, Liang and Fan, Zhonghao}
}

@article{zhao_deep_2018,
	title = {A deep learning model integrating {FCNNs} and {CRFs} for brain tumor segmentation},
	volume = {43},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S136184151730141X},
	doi = {https://doi.org/10.1016/j.media.2017.10.002},
	abstract = {Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks ({FCNNs}) and Conditional Random Fields ({CRFs}) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training {FCNNs} using image patches; 2) training {CRFs} as Recurrent Neural Networks ({CRF}-{RNN}) using image slices with parameters of {FCNNs} fixed; and 3) fine-tuning the {FCNNs} and the {CRF}-{RNN} using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge ({BRATS}) 2013, {BRATS} 2015 and {BRATS} 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.},
	pages = {98 -- 111},
	journaltitle = {Medical Image Analysis},
	author = {Zhao, Xiaomei and Wu, Yihong and Song, Guidong and Li, Zhenye and Zhang, Yazhuo and Fan, Yong},
	date = {2018},
	keywords = {Brain tumor segmentation, Conditional random fields, Deep learning, Fully convolutional neural networks}
}

@article{graham_xy_2018,
	title = {{XY} Network for Nuclear Segmentation in Multi-Tissue Histology Images},
	volume = {abs/1812.06499},
	url = {http://arxiv.org/abs/1812.06499},
	journaltitle = {{CoRR}},
	author = {Graham, Simon and Vu, Quoc Dang and Raza, Shan e Ahmed and Kwak, Jin Tae and Rajpoot, Nasir M.},
	date = {2018},
	note = {\_eprint: 1812.06499}
}
@inproceedings{khagi_alzheimers_2019,
	title = {Alzheimer's disease Classification from Brain {MRI} based on transfer learning from {CNN}},
	doi = {10.1109/BMEiCON.2018.8609974},
	author = {Khagi, Bijen and Lee, Chung and Kwon, Goo-Rak},
	date = {2019}
}

@inproceedings{khvostikov_classification_2017,
	title = {Classification methods on different imaging modalities for Alzheimer disease studies},
	author = {Khvostikov, Alexander and Benois-Pineau, Jenny and Krylov, Andrey and Catheline, Gwenaelle},
	date = {2017}
}

@article{lu_automatic_2017,
	title = {Automatic 3D liver location and segmentation via convolutional neural network and graph cut},
	volume = {12},
	issn = {1861-6429},
	url = {https://doi.org/10.1007/s11548-016-1467-3},
	doi = {10.1007/s11548-016-1467-3},
	abstract = {Segmentation of the liver from abdominal computed tomography ({CT}) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in {CT} scans.},
	pages = {171--182},
	number = {2},
	journaltitle = {International Journal of Computer Assisted Radiology and Surgery},
	author = {Lu, Fang and Wu, Fa and Hu, Peijun and Peng, Zhiyi and Kong, Dexing},
	date = {2017}
}

@article{naylor_segmentation_2019,
	title = {Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map},
	volume = {38},
	doi = {10.1109/TMI.2018.2865709},
	pages = {448--459},
	number = {2},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Naylor, P. and Laé, M. and Reyal, F. and Walter, T.},
	date = {2019},
	keywords = {Biology, Cancer, Cancer research, Computer architecture, Haematoxylin and Eosin stained histopathology data, Image segmentation, Pathology, Task analysis, Tumors, biological tissues, cancer, cell nuclei, cellular biophysics, deep learning, deep regression, digital pathology, diseased tissue, diseases, distance map, fully convolutional networks, histopathology, histopathology data, histopathology images, image segmentation, interpretable models, medical image processing, neural nets, nuclei segmentation, patient diagnosis, prognosis tasks, quantitative profiles, regression task, segmentation problem}
}

@article{tofighi_prior_2019,
	title = {Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection},
	volume = {38},
	doi = {10.1109/TMI.2019.2895318},
	pages = {2047--2058},
	number = {9},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Tofighi, M. and Guo, T. and Vanamala, J. K. P. and Monga, V.},
	date = {2019-09},
	keywords = {Biomedical imaging, Computer architecture, Deep learning, Image edge detection, Image segmentation, Microprocessors, Nucleus detection, Shape, {TSP}-{CNN}, biology computing, canonical cell nuclei shapes, cell nuclei detection, cell nucleus boundary, cell nucleus detection, cellular biophysics, cellular image quality, convolutional neural nets, convolutional neural networks, deep learning, deep learning methods, domain expert, fixed processing part, input images, labeled nuclei locations, learnable layers, learnable shapes, learning (artificial intelligence), medical image processing, morphological processing, multiple cell nuclei, network structures, nuclear morphology, nucleus shapes, regularization terms, shape priors, spatial processing, training set, tunable {SP}-{CNN}}
}

@inproceedings{cai_pancreas_2017,
	location = {Cham},
	title = {Pancreas Segmentation in {MRI} Using Graph-Based Decision Fusion on Convolutional Neural Networks},
	isbn = {978-3-319-66179-7},
	abstract = {Deep neural networks have demonstrated very promising performance on accurate segmentation of challenging organs (e.g., pancreas) in abdominal {CT} and {MRI} scans. The current deep learning approaches conduct pancreas segmentation by processing sequences of 2D image slices independently through deep, dense per-pixel masking for each image, without explicitly enforcing spatial consistency constraint on segmentation of successive slices. We propose a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem. A deep convolutional sub-network is first designed and pre-trained from scratch. The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner. Our recurrent sub-network is a type of Long short-term memory ({LSTM}) network that performs segmentation on an image by integrating its neighboring slice segmentation predictions, in the form of a dependent sequence processing. Additionally, a novel segmentation-direct loss function (named Jaccard Loss) is proposed and deep networks are trained to optimize Jaccard Index ({JI}) directly. Extensive experiments are conducted to validate our proposed deep models, on quantitative pancreas segmentation using both {CT} and {MRI} scans. Our method outperforms the state-of-the-art work on {CT} [11] and {MRI} pancreas segmentation [1], respectively.},
	pages = {674--682},
	booktitle = {Medical Image Computing and Computer Assisted Intervention − {MICCAI} 2017},
	publisher = {Springer International Publishing},
	author = {Cai, Jinzheng and Lu, Le and Xie, Yuanpu and Xing, Fuyong and Yang, Lin},
	editor = {Descoteaux, Maxime and Maier-Hein, Lena and Franz, Alfred and Jannin, Pierre and Collins, D. Louis and Duchesne, Simon},
	date = {2017}
}

@inproceedings{zhou_fixed-point_2017,
	location = {Cham},
	title = {A Fixed-Point Model for Pancreas Segmentation in Abdominal {CT} Scans},
	isbn = {978-3-319-66182-7},
	abstract = {Deep neural networks have been widely adopted for automatic organ segmentation from abdominal {CT} scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the {NIH} pancreas segmentation dataset, and outperform the state-of-the-art by more than \$\$4{\textbackslash}backslash\%\$\$, measured by the average Dice-Sørensen Coefficient ({DSC}). In addition, we report \$\$62.43{\textbackslash}backslash\%\$\${DSC} in the worst case, which guarantees the reliability of our approach in clinical applications.},
	pages = {693--701},
	booktitle = {Medical Image Computing and Computer Assisted Intervention − {MICCAI} 2017},
	publisher = {Springer International Publishing},
	author = {Zhou, Yuyin and Xie, Lingxi and Shen, Wei and Wang, Yan and Fishman, Elliot K. and Yuille, Alan L.},
	editor = {Descoteaux, Maxime and Maier-Hein, Lena and Franz, Alfred and Jannin, Pierre and Collins, D. Louis and Duchesne, Simon},
	date = {2017}
}

@inproceedings{dou_3d_2016,
	location = {Cham},
	title = {3D Deeply Supervised Network for Automatic Liver Segmentation from {CT} Volumes},
	isbn = {978-3-319-46723-8},
	abstract = {Automatic liver segmentation from {CT} volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D {DSN}) to address this challenging task. The proposed 3D {DSN} takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D {DSN}, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public {MICCAI}-{SLiver}07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.},
	pages = {149--157},
	booktitle = {Medical Image Computing and Computer-Assisted Intervention – {MICCAI} 2016},
	publisher = {Springer International Publishing},
	author = {Dou, Qi and Chen, Hao and Jin, Yueming and Yu, Lequan and Qin, Jing and Heng, Pheng-Ann},
	editor = {Ourselin, Sebastien and Joskowicz, Leo and Sabuncu, Mert R. and Unal, Gozde and Wells, William},
	date = {2016}
}

@article{song_multi-layer_2019,
	title = {Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images},
	volume = {176},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S095070511930156X},
	doi = {https://doi.org/10.1016/j.knosys.2019.03.031},
	abstract = {It is a challenging problem to achieve generalized nuclear segmentation in digital histopathology images. Existing techniques, using either handcrafted features in learning-based models or traditional image analysis-based approaches, do not effectively tackle the challenging cases, such as crowded nuclei, chromatin-sparse, and heavy background clutter. In contrast, deep networks have achieved state-of-the-art performance in modeling various nuclear appearances. However, their success is limited due to the size of the considered networks. We solve these problems by reformulating nuclear segmentation in terms of a cascade 2-class classification problem and propose a multi-layer boosting sparse convolutional ({ML}-{BSC}) model. In the proposed {ML}-{BSC} model, discriminative probabilistic binary decision trees ({PBDTs}) are designed as weak learners in each layer to cope with challenging cases. A sparsity-constrained cascade structure enables the {ML}-{BSC} model to improve representation learning. Comparing to the existing techniques, our method can accurately separate individual nuclei in complex histopathology images, and it is more robust against chromatin-sparse and heavy background clutter. An evaluation carried out using three disparate datasets demonstrates the superiority of our method over the state-of-the-art supervised approaches in terms of segmentation accuracy.},
	pages = {40 -- 53},
	journaltitle = {Knowledge-Based Systems},
	author = {Song, Jie and Xiao, Liang and Molaei, Mohsen and Lian, Zhichao},
	date = {2019},
	keywords = {Cascade classification, Multi-layer boosting sparse convolutional model, Nucleus segmentation, Probabilistic binary decision tree, Representation learning}
}

@article{qaiser_fast_2019,
	title = {Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features},
	volume = {55},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841518302688},
	doi = {https://doi.org/10.1016/j.media.2019.03.014},
	abstract = {Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles ({PHPs}). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The {PHPs} are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on a selection of exemplar image patches from a convolution neural network ({CNN}) and patch classification by quantifying the divergence between the {PHPs} of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the {PHPs} and high-level {CNN} features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of {PHPs} and {CNN} features outperform competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet, and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis.},
	pages = {1 -- 14},
	journaltitle = {Medical Image Analysis},
	author = {Qaiser, Talha and Tsang, Yee-Wah and Taniyama, Daiki and Sakamoto, Naoya and Nakane, Kazuaki and Epstein, David and Rajpoot, Nasir},
	date = {2019},
	keywords = {Colorectal (colon) cancer, Computational pathology, Deep learning, Histology image analysis, Persistent homology, Tumor segmentation}
}

@article{jimenez-carretero_graph-cut_2019,
	title = {A graph-cut approach for pulmonary artery-vein segmentation in noncontrast {CT} images},
	volume = {52},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841518308740},
	doi = {https://doi.org/10.1016/j.media.2018.11.011},
	abstract = {Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery–vein ({AV}) segmentation using computed tomography ({CT}) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary {AV} classification using a random forest ({RF}) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary {CT} phantoms, and 26 lungs from noncontrast {CT} images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy ( ∼ 20\%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.},
	pages = {144 -- 159},
	journaltitle = {Medical Image Analysis},
	author = {Jimenez-Carretero, Daniel and Bermejo-Peláez, David and Nardelli, Pietro and Fraga, Patricia and Fraile, Eduardo and Estépar, Raúl San José and Ledesma-Carbayo, Maria J.},
	date = {2019},
	keywords = {Arteries, Artery-vein segmentation, Graph-cuts, Lung, Noncontrast {CT}, Phantoms, Random forest, Veins}
}

@article{liu_automated_2019,
	title = {Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks},
	volume = {58},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841519300970},
	doi = {https://doi.org/10.1016/j.media.2019.101555},
	abstract = {Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis ({CAD}) methods could potentially provide objective suggestions to assist radiologists. However, the performance of existing learning-based approaches is still limited, for direct application of general learning models often ignores critical domain knowledge related to the specific nodule diagnosis. In this study, we propose a novel deep-learning-based {CAD} system, guided by task-specific prior knowledge, for automated nodule detection and classification in ultrasound images. Our proposed {CAD} system consists of two stages. First, a multi-scale region-based detection network is designed to learn pyramidal features for detecting nodules at different feature scales. The region proposals are constrained by the prior knowledge about size and shape distributions of real nodules. Then, a multi-branch classification network is proposed to integrate multi-view diagnosis-oriented features, in which each network branch captures and enhances one specific group of characteristics that were generally used by radiologists. We evaluated and compared our method with the state-of-the-art {CAD} methods and experienced radiologists on two datasets, i.e. Dataset I and Dataset {II}. The detection and diagnostic accuracy on Dataset I were 97.5\% and 97.1\%, respectively. Besides, our {CAD} system also achieved better performance than experienced radiologists on Dataset {II}, with improvements of accuracy for 8\%. The experimental results demonstrate that our proposed method is effective in the discrimination of thyroid nodules.},
	pages = {101555},
	journaltitle = {Medical Image Analysis},
	author = {Liu, Tianjiao and Guo, Qianqian and Lian, Chunfeng and Ren, Xuhua and Liang, Shujun and Yu, Jing and Niu, Lijuan and Sun, Weidong and Shen, Dinggang},
	date = {2019},
	keywords = {Clinical knowledge, Convolutional neural networks, Thyroid nodule, Ultrasound image}
}

@article{wang_semi-supervised_2020,
	title = {Semi-supervised mp-{MRI} data synthesis with {StitchLayer} and auxiliary distance maximization},
	volume = {59},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841519301057},
	doi = {https://doi.org/10.1016/j.media.2019.101565},
	abstract = {The availability of a large amount of annotated data is critical for many medical image analysis applications, in particular for those relying on deep learning methods which are known to be data-hungry. However, annotated medical data, especially multimodal data, is often scarce and costly to obtain. In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging data (i.e. mp-{MRI}), which typically consists of Apparent Diffusion Coefficient ({ADC}) and T2-weighted (T2w) images, containing clinically significant ({CS}) prostate cancer ({PCa}) via semi-supervised learning and adversarial learning. Specifically, our synthesizer generates mp-{MRI} data in a sequential manner: first utilizing a decoder to generate an {ADC} map from a 128-d latent vector, followed by translating the {ADC} to the T2w image via U-Net. The synthesizer is trained in a semi-supervised manner. In the supervised training process, a limited amount of paired {ADC}-T2w images and the corresponding {ADC} encodings are provided and the synthesizer learns the paired relationship by explicitly minimizing the reconstruction losses between synthetic and real images. To avoid overfitting limited {ADC} encodings, an unlimited amount of random latent vectors and unpaired {ADC}-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images. To improve the robustness for training the synthesizer, we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only. A {StitchLayer} is then employed to seamlessly fuse sub-images together in an interlaced manner into a full-size image. In addition, to enforce the synthetic images to indeed contain distinguishable {CS} {PCa} lesions, we propose to also maximize an auxiliary distance of Jensen-Shannon divergence ({JSD}) between {CS} and {nonCS} images. Experimental results show that our method can effectively synthesize a large variety of mp-{MRI} images which contain meaningful {CS} {PCa} lesions, display a good visual quality and have the correct paired relationship between the two modalities of a pair. Compared to the state-of-the-art methods based on adversarial learning (Liu and Tuzel, 2016; Costa et al., 2017), our method achieves a significant improvement in terms of both visual quality and several popular quantitative evaluation metrics.},
	pages = {101565},
	journaltitle = {Medical Image Analysis},
	author = {Wang, Zhiwei and Lin, Yi and Cheng, Kwang-Ting (Tim) and Yang, Xin},
	date = {2020},
	keywords = {Deep learning, {GAN}, Generative models, Multimodal image synthesis}
}

@article{yi_generative_2019,
	title = {Generative adversarial network in medical imaging: A review},
	volume = {58},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841518308430},
	doi = {https://doi.org/10.1016/j.media.2019.101552},
	abstract = {Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.},
	pages = {101552},
	journaltitle = {Medical Image Analysis},
	author = {Yi, Xin and Walia, Ekta and Babyn, Paul},
	date = {2019},
	keywords = {Deep learning, Generative adversarial network, Generative model, Medical imaging, Review}
}

@article{swiderska-chadaj_learning_2019,
	title = {Learning to detect lymphocytes in immunohistochemistry with deep learning},
	volume = {58},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841519300829},
	doi = {https://doi.org/10.1016/j.media.2019.101547},
	abstract = {The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated {CD}3+ and {CD}8+ cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (κ=0.72), whereas the average pathologists agreement with reference standard was κ=0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org.},
	pages = {101547},
	journaltitle = {Medical Image Analysis},
	author = {Swiderska-Chadaj, Zaneta and Pinckaers, Hans and Rijthoven, Mart van and Balkenhol, Maschenka and Melnikova, Margarita and Geessink, Oscar and Manson, Quirine and Sherman, Mark and Polonia, Antonio and Parry, Jeremy and Abubakar, Mustapha and Litjens, Geert and Laak, Jeroen van der and Ciompi, Francesco},
	date = {2019},
	keywords = {Computational pathology, Deep learning, Immune cell detection, Immunohistochemistry}
}

@article{litjens_survey_2017,
	title = {A survey on deep learning in medical image analysis},
	volume = {42},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841517301135},
	doi = {https://doi.org/10.1016/j.media.2017.07.005},
	abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
	pages = {60 -- 88},
	journaltitle = {Medical Image Analysis},
	author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and Laak, Jeroen A. W. M. van der and Ginneken, Bram van and Sánchez, Clara I.},
	date = {2017},
	keywords = {Convolutional neural networks, Deep learning, Medical imaging, Survey}
}

@article{hosny_deep_2018,
	title = {Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study},
	volume = {15},
	url = {https://doi.org/10.1371/journal.pmed.1002711},
	doi = {10.1371/journal.pmed.1002711},
	abstract = {Hugo Aerts and colleagues evaluate the ability of deep learning networks to extract relevant features from computed tomography lung cancer images and stratify patients into low and high mortality risk groups.},
	pages = {1--25},
	number = {11},
	journaltitle = {{PLOS} Medicine},
	author = {Hosny, Ahmed and Parmar, Chintan and Coroller, Thibaud P. and Grossmann, Patrick and Zeleznik, Roman and Kumar, Avnish and Bussink, Johan and Gillies, Robert J. and Mak, Raymond H. and Aerts, Hugo J. W. L.},
	date = {2018},
	note = {Publisher: Public Library of Science}
}

@article{litjens_deep_2016,
	title = {Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis},
	volume = {6},
	doi = {10.1038/srep26286},
	pages = {26286},
	journaltitle = {Scientific Reports},
	author = {Litjens, Geert and Sánchez, Clara and Timofeeva, Nadya and Hermsen, Meyke and Nagtegaal, Iris and Kovacs, Iringo and Hulsbergen-van de Kaa, Christina and Bult, Peter and Ginneken, Bram and van der Laak, Jeroen},
	date = {2016}
}

@article{minati_current_2009,
	title = {Current concepts in Alzheimer's disease: a multidisciplinary review.},
	volume = {24},
	pages = {95--121},
	number = {2},
	journaltitle = {American Journal of Alzheimers Disease and Other Dementias},
	author = {Minati, Ludovico and Edginton, Trudi and Bruzzone, Maria Grazia and Giaccone, Giorgio},
	date = {2009}
}

@article{ju_random_2015,
	title = {Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on {PET}-{CT} Images},
	volume = {24},
	issn = {1057-7149},
	doi = {10.1109/TIP.2015.2488902},
	abstract = {Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography ({PET}) images and low contrast in computed tomography ({CT}) images, segmentation of tumor in the {PET} and {CT} images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of {PET} images and superior spatial resolution of {CT} images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the {PET} and {CT} images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the {PET} and another is for {CT}, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of {PET} and {CT} images, a novel energy representation is devised. For the {PET}, a downhill cost and a 3D derivative cost are proposed. For the {CT}, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 {PET}-{CT} images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the {PET} or {CT} is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.},
	pages = {5854--5867},
	number = {12},
	journaltitle = {{IEEE} Transactions on Image Processing},
	author = {Ju, W. and Xiang, D. and Zhang, B. and Wang, L. and Kopriva, I. and Chen, X.},
	date = {2015},
	keywords = {3D derivative cost, Algorithms, Carcinoma, Computed Tomography ({CT}), Computed tomography, Computer-Assisted, Context, Databases, Factual, Humans, Image Processing, Image segmentation, Lung Neoplasms, Lungs, Non-Small-Cell Lung, {PET}-{CT} imaging, Positron Emission Tomography ({PET}), Positron emission tomography, Positron-Emission Tomography, Three-dimensional displays, Tomography, Tumors, X-Ray Computed, computed tomography ({CT}), computerised tomography, energy function, energy minimization problem, energy representation, graph cut, graph cut segmentation method, image segmentation, interactive segmentation, lung, lung tumor, max-flow-min-cut method, medical image processing, minimisation, positron emission tomography, positron emission tomography ({PET}), prior information, random walk, random walk cosegmentation method, shape penalty, spatial resolution, special link encodes, tumor segmentation, tumours}
}

@article{pereira_brain_2016,
	title = {Brain Tumor Segmentation Using Convolutional Neural Networks in {MRI} Images},
	volume = {35},
	doi = {10.1109/TMI.2016.2538465},
	pages = {1240--1251},
	number = {5},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Pereira, S. and Pinto, A. and Alves, V. and Silva, C. A.},
	date = {2016},
	keywords = {Brain Neoplasms, Brain modeling, Brain tumor, {CNN}-based segmentation methods, Computer-Assisted, Context, Dice similarity coefficient metrics, Glioma, Humans, Image Interpretation, Image segmentation, Kernel, {MRI} images, Machine Learning, Magnetic Resonance Imaging, Magnetic resonance imaging, Neural Networks (Computer), Training, Tumors, automatic segmentation, automatic segmentation methods, biomedical {MRI}, brain, brain tumor segmentation, cancer, clinical practice, convolutional neural networks, data augmentation, deep learning, glioma, gliomas, image segmentation, imaging technique, intensity normalization, kernels, magnetic resonance imaging, manual segmentation, medical image processing, neurophysiology, on-site {BRATS} 2015 Challenge, oncological patients, online evaluation platform, precise quantitative measurements, preprocessing step, quality-of-life, reliable segmentation methods, spatial variability, structural variability, tumours}
}

@article{song_optimal_2013,
	title = {Optimal Co-Segmentation of Tumor in {PET}-{CT} Images With Context Information},
	volume = {32},
	issn = {0278-0062},
	doi = {10.1109/TMI.2013.2263388},
	abstract = {Positron emission tomography ({PET})-computed tomography ({CT}) images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in {PET} or low contrast in {CT}. In this work, we propose a novel method for the co-segmentation of the tumor in both {PET} and {CT} images, which makes use of advantages from each modality: the functionality information from {PET} and the anatomical structure information from {CT}. The approach formulates the segmentation problem as a minimization problem of a Markov random field model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the {PET} and the {CT} images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two sub-graphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 {PET}-{CT} datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using {PET} or {CT}.},
	pages = {1685--1697},
	number = {9},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Song, Q. and Bai, J. and Han, D. and Bhatia, S. and Sun, W. and Rockey, W. and Bayouth, J. E. and Buatti, J. M. and Wu, X.},
	date = {2013-09},
	keywords = {23 {PET}-{CT} dataset, Algorithms, Computational modeling, Computed tomography, Computer-Assisted, Context, Context information, Databases, Factual, Head and Neck Neoplasms, Humans, Image Processing, Image segmentation, Lungs, Markov Chains, Markov processes, Markov random field model, {PET}-{CT} Image, Positron emission tomography, Positron emission tomography-computed tomography ({PET}-{CT}), Positron-Emission Tomography, Tomography, Tumors, X-Ray Computed, adaptive context cost, anatomical structure information, cancer, computerised tomography, context arcs, context information, functionality information, global optimization, graph cut, graph theory, graph-cut based method, head-and-neck cancer subject, image resolution, image segmentation, low spatial resolution, lung, lung tumor, medical image processing, minimisation, minimization problem, optimal solution, positron emission tomography, positron emission tomography-computed tomography image, radiotherapy treatment planning, single imaging modality, single maximum flow problem, subgraph, tumor optimal cosegmentation, tumor volume simultaneous segmentation, tumours}
}

@article{tran_learning_2014,
	title = {Learning Spatiotemporal Features with 3D Convolutional Networks},
	pages = {arXiv:1412.0767},
	journaltitle = {{ArXiv} e-prints},
	author = {Tran, Du and Bourdev, Lubomir and Fergus, Rob and Torresani, Lorenzo and Paluri, Manohar},
	date = {2014},
	note = {\_eprint: 1412.0767},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{han_globally_2011,
	title = {Globally Optimal Tumor Segmentation in {PET}-{CT} Images: A Graph-Based Co-segmentation Method},
	volume = {22},
	doi = {10.1007/978-3-642-22092-0_21},
	pages = {245--56},
	journaltitle = {Information processing in medical imaging : proceedings of the ... conference},
	author = {Han, Dongfeng and Bayouth, John and Song, Qi and Taurani, Aakant and Sonka, Milan and Buatti, John and Wu, Xiaodong},
	date = {2011}
}

@inproceedings{zhong_3d_2018,
	title = {3D fully convolutional networks for co-segmentation of tumors on {PET}-{CT} images},
	doi = {10.1109/ISBI.2018.8363561},
	pages = {228--231},
	booktitle = {2018 {IEEE} 15th International Symposium on Biomedical Imaging ({ISBI} 2018)},
	author = {Zhong, Z. and Kim, Y. and Zhou, L. and Plichta, K. and Allen, B. and Buatti, J. and Wu, X.},
	date = {2018-04},
	note = {{ISSN}: 1945-8452},
	keywords = {3D fully convolutional networks, Biomedical imaging, Computed tomography, Image segmentation, Lung, {PET}-{CT} images, {PET}-{CT} scans, Three-dimensional displays, Tumors, automated accurate tumor delineation, biomedical {MRI}, cancer, cancer diagnosis, co-segmentation, co-segmentation model, computed tomography, computerised tomography, critical diagnostic information, deep learning, dual-modality imaging, final tumor segmentation results, fully convolutional networks, graph cut, image classification, image segmentation, learning (artificial intelligence), lung, lung cancer patients, lung tumor segmentation, medical image processing, positron emission tomography, probability maps, semantic segmentation framework, tumor reading, tumours}
}

@inproceedings{zhong_3d_2017,
	title = {3D Alpha Matting Based Co-segmentation of Tumors on {PET}-{CT} Images},
	booktitle = {{CMMI}/{RAMBO}/{SWITCH}@{MICCAI}},
	author = {Zhong, Zisha and Kim, Yusung and Buatti, John M. and Wu, Xiaodong},
	date = {2017}
}

@article{mahmood_multimodal_2018,
	title = {Multimodal Densenet},
	journaltitle = {{ArXiv} e-prints},
	author = {Mahmood, F. and Yang, Z. and Ashley, T. and Durr, N. J.},
	date = {2018},
	note = {\_eprint: 1811.07407},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning}
}

@article{dolz_hyperdense-net_2018,
	title = {{HyperDense}-Net: A hyper-densely connected {CNN} for multi-modal image segmentation},
	volume = {abs/1804.02967},
	url = {http://arxiv.org/abs/1804.02967},
	journaltitle = {{CoRR}},
	author = {Dolz, Jose and Gopinath, Karthik and Yuan, Jing and Lombaert, Herve and Desrosiers, Christian and Ayed, Ismail Ben},
	date = {2018},
	note = {\_eprint: 1804.02967}
}

@article{kumar_co-learning_2018,
	title = {Co-Learning Feature Fusion Maps from {PET}-{CT} Images of Lung Cancer},
	pages = {arXiv:1810.02492},
	journaltitle = {{ArXiv} e-prints},
	author = {Kumar, Ashnil and Fulham, Michael and Feng, Dagan and Kim, Jinman},
	date = {2018},
	note = {\_eprint: 1810.02492},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{dice_measures_1945,
	title = {Measures of the Amount of Ecologic Association Between Species},
	volume = {26},
	pages = {297--302},
	number = {3},
	journaltitle = {Ecology},
	author = {Dice, Lee R},
	date = {1945}
}

@article{greig_exact_1989,
	title = {Exact Maximum A Posteriori Estimation for Binary Images},
	volume = {51},
	doi = {10.1111/j.2517-6161.1989.tb01764.x},
	pages = {271--279},
	journaltitle = {Journal of the Royal Statistical Society, Series B},
	author = {Greig, D.M. and Porteous, B.T. and Seheult, Allan},
	date = {1989}
}

@article{cortes_support-vector_1995,
	title = {Support-Vector Networks},
	volume = {20},
	pages = {273--297},
	number = {3},
	journaltitle = {Machine Learning},
	author = {Cortes, Corinna and Vapnik, Vladimir},
	date = {1995}
}

@inproceedings{boser_training_1992,
	title = {A Training Algorithm for Optimal Margin Classifiers},
	pages = {144--152},
	booktitle = {Proceedings of the 5th Annual {ACM} Workshop on Computational Learning Theory},
	publisher = {{ACM} Press},
	author = {Boser, Bernhard E. and Guyon, Isabelle M. and Vapnik, Vladimir N.},
	date = {1992}
}

@article{lafferty_conditional_2001,
	title = {Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data},
	pages = {282--289},
	author = {Lafferty, John and Mccallum, Andrew and Pereira, Fernando},
	date = {2001}
}

@book{mitchell_machine_1997,
	location = {New York},
	title = {Machine Learning},
	isbn = {978-0-07-042807-2},
	abstract = {This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This exciting addition to the {McGraw}-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning—including probability and statistics, artificial intelligence, and neural networks—unifying them all in a logical and coherent manner.},
	publisher = {{McGraw}-Hill},
	author = {Mitchell, Tom M.},
	date = {1997},
	keywords = {01624 105 book shelf ai learn algorithm}
}

@book{haykin_neural_2011,
	title = {Neural Network and Learning Machines (third edition)},
	publisher = {China Machine Press and Pearson Education},
	author = {Haykin, Simon},
	translator = {Shen, Furao and Xu, Ye and Zheng, Jun and Chao, Jin},
	date = {2011}
}

@thesis{hsu_practical_2016,
	title = {A Practical Guide to Support Vector Classification},
	institution = {Department of Computer Science National Taiwan University, Taipei 106, Taiwan},
	type = {phdthesis},
	author = {Hsu, Chih-Wei and {Chih-Chung} and Len, Chih-Jen},
	date = {2016-05-19}
}

@online{noauthor_support_2017,
	title = {Support vector machine},
	url = {https://en.wikipedia.org/wiki/Support_vector_machine},
	date = {2017}
}

@book{flach_machine_2012,
	title = {Machine Learning: The Art and Science of Algorithms that Make Sense of Data(first edition)},
	publisher = {Posts Telecom Press and Cambridge University Press},
	author = {Flach, Peter},
	translator = {Duan, Fei},
	date = {2012}
}

@book{sugiyama_graphic_2013,
	title = {Graphic Machine Learning},
	publisher = {Posts Telecom Press and Kodansha {LTD}.},
	author = {Sugiyama, Masashi},
	translator = {X, Yongwei},
	date = {2013}
}

@article{wang_dynamic_2018,
	title = {Dynamic Graph {CNN} for Learning on Point Clouds},
	volume = {abs/1801.07829},
	url = {http://arxiv.org/abs/1801.07829},
	journaltitle = {{CoRR}},
	author = {Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
	date = {2018},
	note = {\_eprint: 1801.07829}
}

@article{simonovsky_dynamic_2017,
	title = {Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs},
	volume = {abs/1704.02901},
	url = {http://arxiv.org/abs/1704.02901},
	journaltitle = {{CoRR}},
	author = {Simonovsky, Martin and Komodakis, Nikos},
	date = {2017},
	note = {\_eprint: 1704.02901}
}

@article{kipf_semi-supervised_2016,
	title = {Semi-Supervised Classification with Graph Convolutional Networks},
	volume = {abs/1609.02907},
	url = {http://arxiv.org/abs/1609.02907},
	journaltitle = {{CoRR}},
	author = {Kipf, Thomas N. and Welling, Max},
	date = {2016},
	note = {\_eprint: 1609.02907}
}

@book{ronneberger_u-net_2015,
	title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	date = {2015},
	note = {\_eprint: 1505.04597}
}

@book{szegedy_inception-v4_2016,
	title = {Inception-v4, Inception-{ResNet} and the Impact of Residual Connections on Learning},
	author = {Szegedy, Christian and Ioffe, Sergey and Vanhoucke, Vincent and Alemi, Alex},
	date = {2016},
	note = {\_eprint: 1602.07261}
}

@incollection{lecun_handbook_1998,
	location = {Cambridge, {MA}, {USA}},
	title = {The Handbook of Brain Theory and Neural Networks},
	isbn = {0-262-51102-9},
	url = {http://dl.acm.org/citation.cfm?id=303568.303704},
	pages = {255--258},
	publisher = {{MIT} Press},
	author = {{LeCun}, Yann and Bengio, Yoshua},
	editor = {Arbib, Michael A.},
	date = {1998},
	note = {Section: Convolutional Networks for Images, Speech, and Time Series}
}

@book{goodfellow_deep_2016,
	title = {Deep Learning},
	publisher = {{MIT} Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	date = {2016}
}

@incollection{krizhevsky_imagenet_2012,
	title = {{ImageNet} Classification with Deep Convolutional Neural Networks},
	url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf},
	pages = {1097--1105},
	booktitle = {Advances in Neural Information Processing Systems 25},
	publisher = {Curran Associates, Inc.},
	author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
	editor = {Pereira, F. and Burges, C. J. C. and Bottou, L. and Weinberger, K. Q.},
	date = {2012}
}

@article{huang_densely_2016,
	title = {Densely Connected Convolutional Networks},
	volume = {abs/1608.06993},
	url = {http://arxiv.org/abs/1608.06993},
	journaltitle = {{CoRR}},
	author = {Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q.},
	date = {2016},
	note = {\_eprint: 1608.06993}
}

@article{lecun_gradient-based_1998,
	title = {Gradient-based learning applied to document recognition},
	volume = {86},
	pages = {2278--2324},
	number = {11},
	journaltitle = {Proceedings of the {IEEE}},
	author = {Lecun, Yann and Bottou, Leon and Bengio, Yoshua and Haffner, Patrick},
	date = {1998}
}

@article{bronstein_geometric_2016,
	title = {Geometric deep learning: going beyond Euclidean data},
	volume = {abs/1611.08097},
	url = {http://arxiv.org/abs/1611.08097},
	journaltitle = {{CoRR}},
	author = {Bronstein, Michael M. and Bruna, Joan and {LeCun}, Yann and Szlam, Arthur and Vandergheynst, Pierre},
	date = {2016},
	note = {\_eprint: 1611.08097}
}

@article{mou_tbcnn_2014,
	title = {{TBCNN}: A Tree-Based Convolutional Neural Network for Programming Language Processing},
	volume = {abs/1409.5718},
	url = {http://arxiv.org/abs/1409.5718},
	journaltitle = {{CoRR}},
	author = {Mou, Lili and Li, Ge and Jin, Zhi and Zhang, Lu and Wang, Tao},
	date = {2014},
	note = {\_eprint: 1409.5718}
}

@article{kuo_understanding_2016,
	title = {Understanding Convolutional Neural Networks with A Mathematical Model},
	volume = {abs/1609.04112},
	url = {http://arxiv.org/abs/1609.04112},
	journaltitle = {{CoRR}},
	author = {Kuo, C.-C. Jay},
	date = {2016},
	note = {\_eprint: 1609.04112}
}

@article{aerts_decoding_2014,
	title = {Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach},
	volume = {5},
	issn = {2041-1723},
	url = {https://doi.org/10.1038/ncomms5006},
	doi = {10.1038/ncomms5006},
	abstract = {Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.},
	pages = {4006},
	number = {1},
	journaltitle = {Nature Communications},
	author = {Aerts, Hugo J. W. L. and Velazquez, Emmanuel Rios and Leijenaar, Ralph T. H. and Parmar, Chintan and Grossmann, Patrick and Carvalho, Sara and Bussink, Johan and Monshouwer, René and Haibe-Kains, Benjamin and Rietveld, Derek and Hoebers, Frank and Rietbergen, Michelle M. and Leemans, C. René and Dekker, Andre and Quackenbush, John and Gillies, Robert J. and Lambin, Philippe},
	date = {2014}
}

@article{singanamalli_identifying_2016,
	title = {Identifying in vivo {DCE} {MRI} markers associated with microvessel architecture and gleason grades of prostate cancer},
	volume = {43},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.24975},
	doi = {10.1002/jmri.24975},
	abstract = {Background To identify computer extracted in vivo dynamic contrast enhanced ({DCE}) {MRI} markers associated with quantitative histomorphometric ({QH}) characteristics of microvessels and Gleason scores ({GS}) in prostate cancer. Methods This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric {MRI} before radical prostatectomy ({RP}). Representative slices from {RP} specimens were stained with vascular marker {CD}31. Tumor extent was mapped from {RP} sections onto {DCE} {MRI} using nonlinear registration methods. Seventy-seven microvessel {QH} features and 18 {DCE} {MRI} kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high {GS}. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of {GS} were examined. Results A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve ({AUC}) {\textgreater} 0.7. These features were most highly correlated with mean washout gradient ({WG}) (max rho = −0.62). Independent analysis revealed {WG} to be moderately robust to temporal resolution (intraclass correlation coefficient [{ICC}] = 0.63) and {WG} variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors ({AUC} = 0.77). Enhancement ratio was the most robust ({ICC} = 0.96) and discriminative ({AUC} = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = −0.52). Conclusion Computer extracted features of prostate {DCE} {MRI} appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high {GS}. J. {MAGN}. {RESON}. {IMAGING} 2016;43:149–158.},
	pages = {149--158},
	number = {1},
	journaltitle = {Journal of Magnetic Resonance Imaging},
	author = {Singanamalli, Asha and Rusu, Mirabela and Sparks, Rachel E. and Shih, Natalie N.C. and Ziober, Amy and Wang, Li-Ping and Tomaszewski, John and Rosen, Mark and Feldman, Michael and Madabhushi, Anant},
	date = {2016},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.24975},
	keywords = {{DCE} {MRI}, Gleason grades, imaging biomarkers, microvessel architecture, prostate cancer, quantitative histomorphometry}
}

@article{rusu_co-registration_2017,
	title = {Co-registration of pre-operative {CT} with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study},
	volume = {27},
	issn = {1432-1084},
	url = {https://doi.org/10.1007/s00330-017-4813-0},
	doi = {10.1007/s00330-017-4813-0},
	abstract = {To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative {CT}, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule.},
	pages = {4209--4217},
	number = {10},
	journaltitle = {European Radiology},
	author = {Rusu, Mirabela and Rajiah, Prabhakar and Gilkeson, Robert and Yang, Michael and Donatelli, Christopher and Thawani, Rajat and Jacono, Frank J. and Linden, Philip and Madabhushi, Anant},
	date = {2017}
}

@book{heller_kits19_2019,
	title = {The {KiTS}19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, {CT} Semantic Segmentations, and Surgical Outcomes},
	author = {Heller, Nicholas and Sathianathen, Niranjan and Kalapara, Arveen and Walczak, Edward and Moore, Keenan and Kaluzniak, Heather and Rosenberg, Joel and Blake, Paul and Rengel, Zachary and Oestreich, Makinna and Dean, Joshua and Tradewell, Michael and Shah, Aneri and Tejpaul, Resha and Edgerton, Zachary and Peterson, Matthew and Raza, Shaneabbas and Regmi, Subodh and Papanikolopoulos, Nikolaos and Weight, Christopher},
	date = {2019},
	note = {\_eprint: 1904.00445}
}

@article{clark_cancer_2013,
	title = {The Cancer Imaging Archive ({TCIA}): Maintaining and Operating a Public Information Repository},
	volume = {26},
	issn = {1618-727X},
	url = {https://doi.org/10.1007/s10278-013-9622-7},
	doi = {10.1007/s10278-013-9622-7},
	abstract = {The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute ({NCI}) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. {NCI} has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive ({TCIA})–an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, {TCIA} accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, {PubMed}, refereed journals) require management and customer service. This paper describes the management tasks and user support model for {TCIA}.},
	pages = {1045--1057},
	number = {6},
	journaltitle = {Journal of Digital Imaging},
	author = {Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and Tarbox, Lawrence and Prior, Fred},
	date = {2013}
}

@article{bilic_liver_2019,
	title = {The Liver Tumor Segmentation Benchmark ({LiTS})},
	volume = {abs/1901.04056},
	url = {http://arxiv.org/abs/1901.04056},
	journaltitle = {{CoRR}},
	author = {Bilic, Patrick and Christ, Patrick Ferdinand and Vorontsov, Eugene and Chlebus, Grzegorz and Chen, Hao and Dou, Qi and Fu, Chi-Wing and Han, Xiao and Heng, Pheng-Ann and Hesser, Jürgen and Kadoury, Samuel and Konopczynski, Tomasz K. and Le, Miao and Li, Chunming and Li, Xiaomeng and Lipková, Jana and Lowengrub, John S. and Meine, Hans and Moltz, Jan Hendrik and Pal, Chris and Piraud, Marie and Qi, Xiaojuan and Qi, Jin and Rempfler, Markus and Roth, Karsten and Schenk, Andrea and Sekuboyina, Anjany and Zhou, Ping and Hülsemeyer, Christian and Beetz, Marcel and Ettlinger, Florian and Grün, Felix and Kaissis, Georgios and Lohöfer, Fabian and Braren, Rickmer and Holch, Julian and Hofmann, Felix and Sommer, Wieland H. and Heinemann, Volker and Jacobs, Colin and Mamani, Gabriel Efrain Humpire and Ginneken, Bram van and Chartrand, Gabriel and Tang, An and Drozdzal, Michal and Ben-Cohen, Avi and Klang, Eyal and Amitai, Michal Marianne and Konen, Eli and Greenspan, Hayit and Moreau, Johan and Hostettler, Alexandre and Soler, Luc and Vivanti, Refael and Szeskin, Adi and Lev-Cohain, Naama and Sosna, Jacob and Joskowicz, Leo and Menze, Bjoern H.},
	date = {2019},
	note = {\_eprint: 1901.04056}
}

@article{rister_ct_2018,
	title = {{CT} organ segmentation using {GPU} data augmentation, unsupervised labels and {IOU} loss},
	volume = {abs/1811.11226},
	url = {http://arxiv.org/abs/1811.11226},
	journaltitle = {{CoRR}},
	author = {Rister, Blaine and Yi, Darvin and Shivakumar, Kaushik and Nobashi, Tomomi and Rubin, Daniel L.},
	date = {2018},
	note = {\_eprint: 1811.11226}
}

@book{armato_iii_samuel_g_data_2015,
	title = {Data From {LIDC}-{IDRI}},
	url = {https://wiki.cancerimagingarchive.net/x/rgAe},
	publisher = {The Cancer Imaging Archive},
	author = {{Armato III, Samuel G.} and McLennan, Geoffrey and Bidaut, Luc and McNitt-Gray, Michael F. and Meyer, Charles R. and Reeves, Anthony P. and Zhao, Binsheng and Aberle, Denise R. and Henschke, Claudia I. and Hoffman, Eric A. and Kazerooni, Ella A. and MacMahon, Heber and Van Beek, Edwin J.R. and Yankelevitz, David and Biancardi, Alberto M. and Bland, Peyton H. and Brown, Matthew S. and Engelmann, Roger M. and Laderach, Gary E. and Max, Daniel and Pais, Richard C. and Qing, David P.Y. and Roberts, Rachael Y. and Smith, Amanda R. and Starkey, Adam and Batra, Poonam and Caligiuri, Philip and Farooqi, Ali and Gladish, Gregory W. and Jude, C. Matilda and Munden, Reginald F. and Petkovska, Iva and Quint, Leslie E. and Schwartz, Lawrence H. and Sundaram, Baskaran and Dodd, Lori E. and Fenimore, Charles and Gur, David and Petrick, Nicholas and Freymann, John and Kirby, Justin and Hughes, Brian and Casteele, Alessi Vande and Gupte, Sangeeta and Sallam, Maha and Heath, Michael D. and Kuhn, Michael H. and Dharaiya, Ekta and Burns, Richard and Fryd, David S. and Salganicoff, Marcos and Anand, Vikram and Shreter, Uri and Vastagh, Stephen and Croft, Barbara Y. and Clarke, Laurence P.},
	date = {2015},
	doi = {10.7937/K9/TCIA.2015.LO9QL9SX}
}

@article{mueller_alzheimers_2005,
	title = {The Alzheimer's Disease Neuroimaging Initiative},
	volume = {15},
	pages = {869--877},
	number = {4},
	journaltitle = {Neuroimaging Clinics of North America},
	author = {Mueller, Susanne G and Weiner, Michael W and Thal, Leon J and Petersen, Ronald Carl and Jack, Clifford R Jr and Jagust, William J and Trojanowski, John Q and Toga, Arthur W and Beckett, Laurel A},
	date = {2005}
}

@article{setio_validation_2016,
	title = {Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The {LUNA}16 challenge},
	volume = {42},
	doi = {10.1016/j.media.2017.06.015},
	journaltitle = {Medical Image Analysis},
	author = {Setio, Arnaud and Traverso, Alberto and Bel, Thomas and Berens, Moira and Bogaard, Cas and Cerello, Piergiorgio and Chen, Hao and Dou, Qi and Fantacci, Maria and Geurts, Bram and Gugten, Robbert and Heng, Pheng and Jansen, Bart and Kaste, Michael and Kotov, Valentin and Lin, Jack and Manders, Jeroen and Sónora-Mengana, Alexander and Naranjo, Juan Carlos and Papavasileiou, Evgenia},
	date = {2016}
}

@inproceedings{vahadane_learning_2016,
	title = {Learning based super-resolution of histological images},
	doi = {10.1109/ISBI.2016.7493391},
	pages = {816--819},
	booktitle = {2016 {IEEE} 13th International Symposium on Biomedical Imaging ({ISBI})},
	author = {Vahadane, A. and Kumar, N. and Sethi, A.},
	date = {2016-04},
	keywords = {Artificial neural networks, Image edge detection, Image reconstruction, Image resolution, Image super-resolution, Testing, Training, histological image, neural network}
}

@article{kumar_dataset_2017,
	title = {A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology},
	volume = {36},
	doi = {10.1109/TMI.2017.2677499},
	pages = {1550--1560},
	number = {7},
	journaltitle = {{IEEE} Transactions on Medical Imaging},
	author = {Kumar, N. and Verma, R. and Sharma, S. and Bhargava, S. and Vahadane, A. and Sethi, A.},
	date = {2017-07},
	keywords = {Algorithms, Annotation, Cell Nucleus, Computer-Assisted, Diseases, H\&E-stained images, Humans, Image Processing, Image color analysis, Image segmentation, Machine Learning, Machine learning, Measurement, Otsu thresholding, Pathology, Staining and Labeling, Training, biological organs, biological tissues, biomedical optical imaging, boundaries, cellular biophysics, chromatin-sparse, computational pathology, conventional image processing techniques, crowded nuclei, dataset, deep learning, digital microscopic tissue images, disease states, diseases, feature extraction, generalized nuclear segmentation, hematoxylin and eosin-stained tissue images, high-quality feature extraction, image classification, image classification problems, image segmentation, learning (artificial intelligence), machine learning algorithms, machine learning-based segmentation, medical image processing, nuclear appearances, nuclear boundaries, nuclear morphometrics, nuclear segmentation, nuclei, object recognition, object-level errors, optical microscopy, organs, overlapping nuclei, pixel-level errors, right out-of-the-box, segmentation technique, watershed segmentation}
}

@inproceedings{hackel_semantic3dnet_2017,
	title = {{SEMANTIC}3D.{NET}: A new large-scale point cloud classification benchmark},
	volume = {{IV}-1-W1},
	pages = {91--98},
	booktitle = {{ISPRS} Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
	author = {Hackel, Timo and Savinov, N. and Ladicky, L. and Wegner, Jan D. and Schindler, K. and Pollefeys, M.},
	date = {2017}
}

@article{wu_3d_2014,
	title = {3D {ShapeNets}: A Deep Representation for Volumetric Shapes},
	journaltitle = {{ArXiv} e-prints},
	author = {Wu, Z. and Song, S. and Khosla, A. and Yu, F. and Zhang, L. and Tang, X. and Xiao, J.},
	date = {2014},
	note = {\_eprint: 1406.5670},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{chang_shapenet_2015,
	title = {{ShapeNet}: An Information-Rich 3D Model Repository},
	volume = {abs/1512.03012},
	url = {http://arxiv.org/abs/1512.03012},
	journaltitle = {{CoRR}},
	author = {Chang, Angel X. and Funkhouser, Thomas A. and Guibas, Leonidas J. and Hanrahan, Pat and Huang, Qi-Xing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
	date = {2015},
	note = {\_eprint: 1512.03012}
}

@article{geiger_vision_2013,
	title = {Vision meets Robotics: The {KITTI} Dataset},
	journaltitle = {International Journal of Robotics Research ({IJRR})},
	author = {Geiger, Andreas and Lenz, Philip and Stiller, Christoph and Urtasun, Raquel},
	date = {2013}
}

@article{vallet_terramobilitaiqmulus_2015,
	title = {{TerraMobilita}/{iQmulus} Urban Point Cloud Analysis Benchmark},
	volume = {49},
	journaltitle = {Computers \& Graphics},
	author = {Vallet, Bruno and Brédif, Mathieu and Serna, Andrés and Marcotegui, B and Paparoditis, Nicolas},
	date = {2015}
}

@article{goodfellow_challenges_2015,
	title = {Challenges in representation learning: A report on three machine learning contests},
	volume = {64},
	issn = {0893-6080},
	url = {http://www.sciencedirect.com/science/article/pii/S0893608014002159},
	doi = {https://doi.org/10.1016/j.neunet.2014.09.005},
	abstract = {The {ICML} 2013 Workshop on Challenges in Representation Learning11http://deeplearning.net/icml2013-workshop-competition. focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.},
	pages = {59 -- 63},
	journaltitle = {Neural Networks},
	author = {Goodfellow, Ian J. and Erhan, Dumitru and Carrier, Pierre Luc and Courville, Aaron and Mirza, Mehdi and Hamner, Ben and Cukierski, Will and Tang, Yichuan and Thaler, David and Lee, Dong-Hyun and Zhou, Yingbo and Ramaiah, Chetan and Feng, Fangxiang and Li, Ruifan and Wang, Xiaojie and Athanasakis, Dimitris and Shawe-Taylor, John and Milakov, Maxim and Park, John and Ionescu, Radu and Popescu, Marius and Grozea, Cristian and Bergstra, James and Xie, Jingjing and Romaszko, Lukasz and Xu, Bing and Chuang, Zhang and Bengio, Yoshua},
	date = {2015},
	keywords = {Competition, Dataset, Representation learning}
}

@inproceedings{madani_chest_2018,
	title = {Chest x-ray generation and data augmentation for cardiovascular abnormality classification},
	volume = {10574},
	url = {https://doi.org/10.1117/12.2293971},
	doi = {10.1117/12.2293971},
	pages = {415 -- 420},
	booktitle = {Medical Imaging 2018: Image Processing},
	publisher = {{SPIE}},
	author = {Madani, Ali and Moradi, Mehdi and Karargyris, Alexandros and Syeda-Mahmood, Tanveer},
	editor = {Angelini, Elsa D. and Landman, Bennett A.},
	date = {2018},
	note = {Backup Publisher: International Society for Optics and Photonics},
	keywords = {Convolutional networks, Generative adversarial networks, data augmentation}
}

@article{takahashi_data_2019,
	title = {Data Augmentation using Random Image Cropping and Patching for Deep {CNNs}},
	issn = {1558-2205},
	url = {http://dx.doi.org/10.1109/TCSVT.2019.2935128},
	doi = {10.1109/tcsvt.2019.2935128},
	pages = {1--1},
	journaltitle = {{IEEE} Transactions on Circuits and Systems for Video Technology},
	author = {Takahashi, Ryo and Matsubara, Takashi and Uehara, Kuniaki},
	date = {2019},
	note = {Publisher: Institute of Electrical and Electronics Engineers ({IEEE})}
}

@book{zhong_random_2017,
	title = {Random Erasing Data Augmentation},
	author = {Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
	date = {2017},
	note = {\_eprint: 1708.04896}
}

@book{summers_improved_2018,
	title = {Improved Mixed-Example Data Augmentation},
	author = {Summers, Cecilia and Dinneen, Michael J.},
	date = {2018},
	note = {\_eprint: 1805.11272}
}

@inproceedings{jurio_comparison_2010,
	location = {Berlin, Heidelberg},
	title = {A Comparison Study of Different Color Spaces in Clustering Based Image Segmentation},
	isbn = {978-3-642-14058-7},
	abstract = {In this work we carry out a comparison study between different color spaces in clustering-based image segmentation. We use two similar clustering algorithms, one based on the entropy and the other on the ignorance. The study involves four color spaces and, in all cases, each pixel is represented by the values of the color channels in that space. Our purpose is to identify the best color representation, if there is any, when using this kind of clustering algorithms.},
	pages = {532--541},
	booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications},
	publisher = {Springer Berlin Heidelberg},
	author = {Jurio, Aranzazu and Pagola, Miguel and Galar, Mikel and Lopez-Molina, Carlos and Paternain, Daniel},
	editor = {Hüllermeier, Eyke and Kruse, Rudolf and Hoffmann, Frank},
	date = {2010}
}

@book{chatfield_return_2014,
	title = {Return of the Devil in the Details: Delving Deep into Convolutional Nets},
	author = {Chatfield, Ken and Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
	date = {2014},
	note = {\_eprint: 1405.3531}
}

@article{shorten_survey_2019,
	title = {A survey on Image Data Augmentation for Deep Learning},
	volume = {6},
	issn = {2196-1115},
	url = {https://doi.org/10.1186/s40537-019-0197-0},
	doi = {10.1186/s40537-019-0197-0},
	abstract = {Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on {GANs} are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.},
	pages = {60},
	number = {1},
	journaltitle = {Journal of Big Data},
	author = {Shorten, Connor and Khoshgoftaar, Taghi M.},
	date = {2019}
}

@article{yi_syncspeccnn_2016,
	title = {{SyncSpecCNN}: Synchronized Spectral {CNN} for 3D Shape Segmentation},
	volume = {abs/1612.00606},
	url = {http://arxiv.org/abs/1612.00606},
	journaltitle = {{CoRR}},
	author = {Yi, Li and Su, Hao and Guo, Xingwen and Guibas, Leonidas J.},
	date = {2016},
	note = {\_eprint: 1612.00606}
}

@inproceedings{engelcke_vote3deep_2017,
	title = {Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks},
	url = {https://doi.org/10.1109/ICRA.2017.7989161},
	doi = {10.1109/ICRA.2017.7989161},
	pages = {1355--1361},
	booktitle = {2017 {IEEE} International Conference on Robotics and Automation, {ICRA} 2017, Singapore, Singapore, May 29 - June 3, 2017},
	author = {Engelcke, Martin and Rao, Dushyant and Wang, Dominic Zeng and Tong, Chi Hay and Posner, Ingmar},
	date = {2017}
}

@article{rippel_spectral_2015,
	title = {Spectral Representations for Convolutional Neural Networks},
	journaltitle = {{ArXiv} e-prints},
	author = {Rippel, O. and Snoek, J. and Adams, R. P.},
	date = {2015},
	note = {\_eprint: 1506.03767},
	keywords = {Computer Science - Learning, Statistics - Machine Learning}
}

@article{li_pointcnn_2018,
	title = {{PointCNN}},
	volume = {abs/1801.07791},
	url = {http://arxiv.org/abs/1801.07791},
	journaltitle = {{CoRR}},
	author = {Li, Yangyan and Bu, Rui and Sun, Mingchao and Chen, Baoquan},
	date = {2018},
	note = {\_eprint: 1801.07791}
}

@article{qi_pointnet_2017,
	title = {{PointNet}++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space},
	volume = {abs/1706.02413},
	url = {http://arxiv.org/abs/1706.02413},
	journaltitle = {{CoRR}},
	author = {Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J.},
	date = {2017},
	note = {\_eprint: 1706.02413}
}

@article{qi_pointnet_2016,
	title = {{PointNet}: Deep Learning on Point Sets for 3D Classification and Segmentation},
	volume = {abs/1612.00593},
	url = {http://arxiv.org/abs/1612.00593},
	journaltitle = {{CoRR}},
	author = {Qi, Charles Ruizhongtai and Su, Hao and Mo, Kaichun and Guibas, Leonidas J.},
	date = {2016},
	note = {\_eprint: 1612.00593}
}

@article{cai_pancreas_2018,
	title = {Pancreas Segmentation in {CT} and {MRI} Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning},
	journaltitle = {{ArXiv} e-prints},
	author = {Cai, J. and Lu, L. and Xing, F. and Yang, L.},
	date = {2018},
	note = {\_eprint: 1803.11303},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{shao_h-cnn_2018,
	title = {H-{CNN}: Spatial Hashing Based {CNN} for 3D Shape Analysis},
	journaltitle = {{ArXiv} e-prints},
	author = {Shao, T. and Yang, Y. and Weng, Y. and Hou, Q. and Zhou, K.},
	date = {2018},
	note = {\_eprint: 1803.11385},
	keywords = {Computer Science - Graphics}
}

@article{su_splatnet_2018,
	title = {{SPLATNet}: Sparse Lattice Networks for Point Cloud Processing},
	volume = {abs/1802.08275},
	url = {http://arxiv.org/abs/1802.08275},
	journaltitle = {{CoRR}},
	author = {Su, Hang and Jampani, Varun and Sun, Deqing and Maji, Subhransu and Kalogerakis, Evangelos and Yang, Ming-Hsuan and Kautz, Jan},
	date = {2018},
	note = {\_eprint: 1802.08275}
}

@article{tchapmi_segcloud_2017,
	title = {{SEGCloud}: Semantic Segmentation of 3D Point Clouds},
	volume = {abs/1710.07563},
	url = {http://arxiv.org/abs/1710.07563},
	journaltitle = {{CoRR}},
	author = {Tchapmi, Lyne P. and Choy, Christopher B. and Armeni, Iro and Gwak, {JunYoung} and Savarese, Silvio},
	date = {2017},
	note = {\_eprint: 1710.07563}
}

@article{song_semantic_2017,
	title = {Semantic Scene Completion from a Single Depth Image},
	journaltitle = {{IEEE} Conference on Computer Vision and Pattern Recognition},
	author = {Song, Shuran and Yu, Fisher and Zeng, Andy and Chang, Angel X and Savva, Manolis and Funkhouser, Thomas},
	date = {2017}
}

@article{sharma_vconv-dae_2016,
	title = {{VConv}-{DAE}: Deep Volumetric Shape Learning Without Object Labels},
	volume = {abs/1604.03755},
	url = {http://arxiv.org/abs/1604.03755},
	journaltitle = {{CoRR}},
	author = {Sharma, Abhishek and Grau, Oliver and Fritz, Mario},
	date = {2016},
	note = {\_eprint: 1604.03755}
}

@article{sedaghat_orientation-boosted_2016,
	title = {Orientation-boosted Voxel Nets for 3D Object Recognition},
	volume = {abs/1604.03351},
	url = {http://arxiv.org/abs/1604.03351},
	journaltitle = {{CoRR}},
	author = {Sedaghat, Nima and Zolfaghari, Mohammadreza and Brox, Thomas},
	date = {2016},
	note = {\_eprint: 1604.03351}
}

@article{wu_3d_2014-1,
	title = {3D for 2.5D Object Recognition and Next-Best-View Prediction},
	volume = {abs/1406.5670},
	url = {http://arxiv.org/abs/1406.5670},
	journaltitle = {{CoRR}},
	author = {Wu, Zhirong and Song, Shuran and Khosla, Aditya and Tang, Xiaoou and Xiao, Jianxiong},
	date = {2014},
	note = {\_eprint: 1406.5670}
}

@article{brock_generative_2016,
	title = {Generative and Discriminative Voxel Modeling with Convolutional Neural Networks},
	volume = {abs/1608.04236},
	url = {http://arxiv.org/abs/1608.04236},
	journaltitle = {{CoRR}},
	author = {Brock, André and Lim, Theodore and Ritchie, James M. and Weston, Nick},
	date = {2016},
	note = {\_eprint: 1608.04236}
}

@incollection{tran_deep_2016,
	location = {United States},
	title = {Deep End2End Voxel2Voxel Prediction},
	abstract = {Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been in the area of video classification and detection, i.e., problems involving the prediction of a single class label or a handful of output variables per video. Furthermore, while deep networks are commonly recognized as the best models to use in these domains, there is a widespread perception that in order to yield successful results they often require time-consuming architecture search, manual tweaking of parameters and computationally intensive preprocessing or post-processing methods. In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video. Most importantly, we show that the same exact architecture can be used to achieve competitive results on three widely different voxel-prediction tasks: video semantic segmentation, optical flow estimation, and video coloring. The three networks learned on these problems are trained from raw video without any form of preprocessing and their outputs do not require post-processing to achieve outstanding performance. Thus, they offer an efficient alternative to traditional and much more computationally expensive methods in these video domains.},
	pages = {402--409},
	booktitle = {Proceedings - 29th {IEEE} Conference on Computer Vision and Pattern Recognition Workshops, {CVPRW} 2016},
	publisher = {{IEEE} Computer Society},
	author = {Tran, Du and Bourdev, Lubomir and Fergus, Rob and Torresani, Lorenzo and Paluri, Manohar},
	date = {2016},
	doi = {10.1109/CVPRW.2016.57}
}

@article{meagher_geometric_1982,
	title = {Geometric Modeling Using Octree-Encoding},
	volume = {19},
	pages = {129--147},
	journaltitle = {Computer Graphics and Image Processing},
	author = {Meagher, Donald},
	date = {1982}
}

@inproceedings{maturana_voxnet_2015,
	title = {{VoxNet}: A 3D Convolutional Neural Network for real-time object recognition},
	url = {https://doi.org/10.1109/IROS.2015.7353481},
	doi = {10.1109/IROS.2015.7353481},
	pages = {922--928},
	booktitle = {2015 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems, {IROS} 2015, Hamburg, Germany, September 28 - October 2, 2015},
	author = {Maturana, Daniel and Scherer, Sebastian},
	date = {2015}
}

@inproceedings{kim_3d_2013,
	title = {3D Scene Understanding by Voxel-{CRF}},
	url = {https://doi.org/10.1109/ICCV.2013.180},
	doi = {10.1109/ICCV.2013.180},
	pages = {1425--1432},
	booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013},
	author = {Kim, Byung-soo and Kohli, Pushmeet and Savarese, Silvio},
	date = {2013}
}

@book{noauthor_ieee_2013,
	title = {{IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013},
	isbn = {978-1-4799-2839-2},
	url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6750807},
	publisher = {{IEEE} Computer Society},
	date = {2013}
}
@article{kamnitsas_efficient_2016,
	title = {Efficient Multi-Scale 3D {CNN} with Fully Connected {CRF} for Accurate Brain Lesion Segmentation},
	volume = {abs/1603.05959},
	url = {http://arxiv.org/abs/1603.05959},
	journaltitle = {{CoRR}},
	author = {Kamnitsas, Konstantinos and Ledig, Christian and Newcombe, Virginia F. J. and Simpson, Joanna P. and Kane, Andrew D. and Menon, David K. and Rueckert, Daniel and Glocker, Ben},
	date = {2016},
	note = {\_eprint: 1603.05959}
}

@inproceedings{huang_point_2016,
	title = {Point cloud labeling using 3D Convolutional Neural Network},
	doi = {10.1109/ICPR.2016.7900038},
	pages = {2670--2675},
	booktitle = {2016 23rd International Conference on Pattern Recognition ({ICPR})},
	author = {Huang, Jing and You, Suya},
	date = {2016},
	keywords = {3D convolutional neural network, 3D point cloud labeling scheme, Labeling, Neural networks, Testing, Three-dimensional displays, Training, Training data, Two dimensional displays, computer vision, data handling, neural nets, object recognition, testing process, training process, urban point cloud dataset}
}

@article{riegler_octnet_2016,
	title = {{OctNet}: Learning Deep 3D Representations at High Resolutions},
	volume = {abs/1611.05009},
	url = {http://arxiv.org/abs/1611.05009},
	journaltitle = {{CoRR}},
	author = {Riegler, Gernot and Ulusoy, Ali Osman and Geiger, Andreas},
	date = {2016},
	note = {\_eprint: 1611.05009}
}

@inproceedings{pratikakis_unstructured_2017,
	title = {Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks},
	isbn = {978-3-03868-030-7},
	doi = {10.2312/3dor.20171047},
	booktitle = {Eurographics Workshop on 3D Object Retrieval},
	publisher = {The Eurographics Association},
	author = {Boulch, Alexandre and Saux, Bertrand Le and Audebert, Nicolas},
	editor = {Pratikakis, Ioannis and Dupont, Florent and Ovsjanikov, Maks},
	date = {2017},
	note = {{ISSN}: 1997-0471}
}

@article{wang_o-cnn_2017,
	title = {O-{CNN}: octree-based convolutional neural networks for 3D shape analysis},
	volume = {36},
	url = {http://doi.acm.org/10.1145/3072959.3073608},
	doi = {10.1145/3072959.3073608},
	pages = {72:1--72:11},
	number = {4},
	journaltitle = {{ACM} Trans. Graph.},
	author = {Wang, Peng-Shuai and Liu, Yang and Guo, Yu-Xiao and Sun, Chun-Yu and Tong, Xin},
	date = {2017}
}

@article{ghiasi_laplacian_2016,
	title = {Laplacian Reconstruction and Refinement for Semantic Segmentation},
	volume = {abs/1605.02264},
	url = {http://arxiv.org/abs/1605.02264},
	journaltitle = {{CoRR}},
	author = {Ghiasi, Golnaz and Fowlkes, Charless C.},
	date = {2016},
	note = {\_eprint: 1605.02264}
}

@incollection{lecun_generalization_1989,
	title = {Generalization and network design strategies},
	booktitle = {Connectionism in perspective},
	publisher = {Elsevier},
	author = {Lecun, Yann},
	editor = {Pfeifer, R. and Schreter, Z. and Fogelman, F. and Steels, L.},
	date = {1989}
}

@article{xie_deep3d_2016,
	title = {Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks},
	volume = {abs/1604.03650},
	url = {http://arxiv.org/abs/1604.03650},
	journaltitle = {{CoRR}},
	author = {Xie, Junyuan and Girshick, Ross B. and Farhadi, Ali},
	date = {2016},
	note = {\_eprint: 1604.03650}
}

@article{zamir_generic_2017,
	title = {Generic 3D Representation via Pose Estimation and Matching},
	volume = {abs/1710.08247},
	url = {http://arxiv.org/abs/1710.08247},
	journaltitle = {{CoRR}},
	author = {Zamir, Amir Roshan and Wekel, Tilman and Agrawal, Pulkit and Wei, Colin and Malik, Jitendra and Savarese, Silvio},
	date = {2017},
	note = {\_eprint: 1710.08247}
}

@article{su_multi-view_2015,
	title = {Multi-view Convolutional Neural Networks for 3D Shape Recognition},
	volume = {abs/1505.00880},
	url = {http://arxiv.org/abs/1505.00880},
	journaltitle = {{CoRR}},
	author = {Su, Hang and Maji, Subhransu and Kalogerakis, Evangelos and Learned-Miller, Erik G.},
	date = {2015},
	note = {\_eprint: 1505.00880}
}

@article{wu_single_2016,
	title = {Single Image 3D Interpreter Network},
	volume = {abs/1604.08685},
	url = {http://arxiv.org/abs/1604.08685},
	journaltitle = {{CoRR}},
	author = {Wu, Jiajun and Xue, Tianfan and Lim, Joseph J. and Tian, Yuandong and Tenenbaum, Joshua B. and Torralba, Antonio and Freeman, William T.},
	date = {2016},
	note = {\_eprint: 1604.08685}
}

@article{tatarchenko_single-view_2015,
	title = {Single-view to Multi-view: Reconstructing Unseen Views with a Convolutional Network},
	volume = {abs/1511.06702},
	url = {http://arxiv.org/abs/1511.06702},
	journaltitle = {{CoRR}},
	author = {Tatarchenko, Maxim and Dosovitskiy, Alexey and Brox, Thomas},
	date = {2015},
	note = {\_eprint: 1511.06702}
}

@article{he_deep_2015,
	title = {Deep Residual Learning for Image Recognition},
	volume = {abs/1512.03385},
	url = {http://arxiv.org/abs/1512.03385},
	journaltitle = {{CoRR}},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	date = {2015},
	note = {\_eprint: 1512.03385}
}

@article{flynn_deepstereo_2015,
	title = {{DeepStereo}: Learning to Predict New Views from the World's Imagery},
	volume = {abs/1506.06825},
	url = {http://arxiv.org/abs/1506.06825},
	journaltitle = {{CoRR}},
	author = {Flynn, John and Neulander, Ivan and Philbin, James and Snavely, Noah},
	date = {2015},
	note = {\_eprint: 1506.06825}
}