update

ReadME.md

@@ -125,12 +125,18 @@ e.g.
 - A model trained on UKBB data (SAX slices) with adversarial data augmentation (adversarial noise, adversarial bias field, adversarial morphological deformation, and adversarial affine transformation) is available.
 - This model is expected with improved robustness especially for right ventricle segmentation on cross-domain data. See below test results on intra domain test set (UKBB) and _unseen_ cross domain sets ACDC and M\&Ms.
 
-| Testing config <br> (batch_size=1, roi =256) | UKBB test (600) |        |        | ACDC (100) |        |        | M\&Ms (150) |        |        |
+<!-- | Testing config <br> (batch_size=1, roi =256) | UKBB test (600) |        |        | ACDC (100) |        |        | M\&Ms (150) |        |        |
 | :------------------------------------------: | :-------------: | :----: | :----: | :--------: | :----: | :----: | :---------: | :----: | :----: | ---- | --------------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | --- | ----------------------------------------------------------------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | --- | -------------------------------------------------------------------------------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | --- |
 |                model: UNet_64                |       LV        |  MYO   |   RV   |     LV     |  MYO   |   RV   |     LV      |  MYO   |   RV   |
-|                   baseline                   |     0.9383      | 0.8780 | 0.8979 |   0.8940   | 0.8034 | 0.8237 |   0.8862    | 0.7889 | 0.8168 | <!-- | Finetune w. random DA | 0.9378 | 0.8768 | 0.8975 | 0.8884 | 0.7981 | 0.8295 | 0.8846 | 0.7893 | 0.8158 |     | Finetune w. random DA + adv Bias:<br>UNet*LVSA_Adv_bias*(epochs=20).pth | 0.9326 | 0.8722 | 0.8973 | 0.8809 | 0.7912 | 0.8395 | 0.8794 | 0.7812 | 0.8228 |     | Finetune w. random DA + adv Composite DA:<br><br>UNet*LVSA_Adv_Compose*(epochs=20).pth | 0.9360 | 0.8726 | 0.8966 | 0.8984 | 0.7973 | 0.8440 | 0.8873 | 0.7859 | 0.8343 | --> |
-
-| Finetune w. random DA + adv chain:<br><br> (checkpoints/LVSA/Unet_adv_chain.pth) | 0.9360 | 0.8732 | 0.8965 | 0.9060 | 0.8087 | 0.8404 | 0.8929 | 0.7987 | 0.8245 |
+|                   baseline                   |     0.9383      | 0.8780 | 0.8979 |   0.8940   | 0.8034 | 0.8237 |   0.8862    | 0.7889 | 0.8168 | | Finetune w. random DA | 0.9378 | 0.8768 | 0.8975 | 0.8884 | 0.7981 | 0.8295 | 0.8846 | 0.7893 | 0.8158 |     | Finetune w. random DA + adv Bias:<br>UNet*LVSA_Adv_bias*(epochs=20).pth | 0.9326 | 0.8722 | 0.8973 | 0.8809 | 0.7912 | 0.8395 | 0.8794 | 0.7812 | 0.8228 |     | Finetune w. random DA + adv Composite DA:<br><br>UNet*LVSA_Adv_Compose*(epochs=20).pth | 0.9360 | 0.8726 | 0.8966 | 0.8984 | 0.7973 | 0.8440 | 0.8873 | 0.7859 | 0.8343 | |
+<!--
+| Finetune w. random DA + adv chain:<br><br> (checkpoints/LVSA/Unet_adv_chain.pth) | 0.9360 | 0.8732 | 0.8965 | 0.9060 | 0.8087 | 0.8404 | 0.8929 | 0.7987 | 0.8245 | -->
+
+|               | UKBB   |        |        | ACDC   |        |        | M&Ms   |        |        |
+| ------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
+|               | LV     | MYO    | RV     | LV     | MYO    | RV     | LV     | MYO    | RV     |
+| w/o Adv chain | 0.9383 | 0.8780 | 0.8979 | 0.8940 | 0.8034 | 0.8237 | 0.8862 | 0.7889 | 0.8168 |
+| w/ Adv chain  | 0.9360 | 0.8732 | 0.8965 | 0.9060 | 0.8087 | 0.8404 | 0.8929 | 0.7987 | 0.8245 |
 
 - To deploy the model for segmentation, please run the following command to test first:
   - run `source ./demo_scripts/predict_test.sh`