Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation

This repository provides the code for "Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation". Our work is accepted by MedIA mia_link.

Fig. 1. Structure of ODADA.

Requirementss

Some important required packages include:

• Pytorch version >=0.4.1.
• Python == 3.7
• Some basic python packages such as Numpy.

Follow official guidance to install Pytorch.

Dataset

This Repository contains a toy dataset (HK and BIDMC) for reimplement. You can download a full-version dataset via https://drive.google.com/drive/folders/1KEomtcpTUYCc94nAvEBBsT3vvLnR4rPN?usp=share_link If the data violates privacy, please let us know in time.

Usages

For multi-site prastate segmentation

1. To train ODADA for multi-site prostate segmentation, run:

python main.py

2. Our experimental results are shown in the table:

Citation

If you find our work is helpful for your research, please consider to cite:

@article{sun2022rethinking,
  title={Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation},
  author={Sun, Yongheng and Dai, Duwei and Xu, Songhua},
  journal={Medical Image Analysis},
  pages={102623},
  year={2022},
  publisher={Elsevier}
}