Added published COVIDNet CXR-S severity assessment paper for reference

README.md

@@ -32,7 +32,8 @@ The COVID-19 pandemic continues to have a devastating effect on the health and w
 
 For a detailed description of the methodology behind COVID-Net and a full description of the COVIDx dataset, please click [here](https://www.nature.com/articles/s41598-020-76550-z).
 
-For a detailed description of the methodology behind COVID-Net based deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity, please click [here](https://arxiv.org/abs/2005.12855).
+For a detailed description of the methodology behind COVIDNet CXR-S severity assessment, please click [here](https://www.mdpi.com/2075-4418/12/1/25). \
+For more information on COVIDNet-S deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity, please click [here](https://arxiv.org/abs/2005.12855).
 
 For a detailed description of the methodology behind COVIDNet-CT and the associated dataset of 104,009 CT images from 1,489 patients, please click [here](https://github.com/haydengunraj/COVIDNet-CT).
 

docs/covidnet_severity.md

@@ -1,7 +1,9 @@
 # COVIDNet CXR-S Air Space Severity Grading
-COVDNet CXR-S model takes as input a chest x-ray image of shape (N, 480, 480, 3). where N is the number of batches, 
+COVIDNet CXR-S model takes as input a chest x-ray image of shape (N, 480, 480, 3). where N is the number of batches, 
 and outputs the airspace severity of a SARS-CoV-2 positive patient. The airspace severity is grouped into two levels: 1) Level 1: opacities in 1-2 lung zones, and 2) Level 2: opacities in 3 or more lung zones.
 
+For a detailed description on the methodology behind COVIDNet CXR-S, please click [here](https://www.mdpi.com/2075-4418/12/1/25).
+
 If using the TF checkpoints, here are some useful tensors:
 
 * input tensor: `input_1:0`
@@ -26,6 +28,7 @@ python train_tf.py \
     --datadir data_sev \
     --trainfile labels/train_COVIDxSev.txt \
     --testfile labels/test_COVIDxSev.txt \
+    --in_tensorname input_1:0 \
     --out_tensorname norm_dense_2/Softmax:0 \
     --logit_tensorname norm_dense_2/MatMul:0 \
     --is_severity_model
@@ -45,6 +48,7 @@ python eval.py \
     --n_classes 2 \
     --testfolder data_sev/test \
     --testfile labels/test_COVIDxSev.txt \
+    --in_tensorname input_1:0 \
     --out_tensorname norm_dense_2/Softmax:0 \
     --is_severity_model
 ```
@@ -63,6 +67,7 @@ python inference.py \
     --ckptname model \
     --n_classes 2 \
     --imagepath assets/ex-covid.jpeg \
+    --in_tensorname input_1:0 \
     --out_tensorname norm_dense_2/Softmax:0 \
     --is_severity_model
 ```