emma-sjwang/posal

Code for TMI paper: Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

posal's Issues

What is your specific method to draw ROC curve?

The label of source and label

Thanks for your code. I have a question. In the function def GD_Gene( ), if sourcre: label =0 else: label = 1. Is the label reversed？

No module named 'Model'

ImportError: No module named 'Model'

evaluate_segmentation_results missing

Thanks for your great work, but when i am testing the model, the evaluate_segmentation_results function is missing. Could you upload it at you convenience? Thank you very much.

Coding missing~

I‘am very interested in your perfect work. After scaning it, I found some important code missing.

In the file of test_DGS.py,
Line 22, from Utils.utils import save_img, save_per_img
Line 23, from Utils.evaluate_segmentation import evaluate_segmentation_results

save_per_img and evaluate_segmentation_results are missing implementation.
I'am looking forward to receive your updating

I have a problem of the dataset.

I found that not all of the OD regions are covered by the cropped ROI in Drishti-GS dataset provided by you. Why?

auc or roc curve?

Sorry to bother you again? How is the ROC curve drawn? Is the credibility of the vCDR value?

Coding missing~

I‘am very interested in your perfect work. After scanning it, I found that the part of training the segmentation network with source domain images and annotations is missing.
In the file of train_DGS.py,
Line 64, load_from = "./weights/weights1.h5"

And I tried to train the DGS directly rather than load the weights, I found my results is not so good. The MAE CDR is 0.21, much larger than 0.082. And the Dice optic cup is 0.58 which is much smaller than 0.858. I think maybe it's because I didn't train the segmentation network with source domain images and annotations firstly.
Can you share the code of training the segmentation network or the result of the weights or the weights.h5 file after the training before the train_DGS?
Thanks very much.