DR-segmentation

Open-sourced code for the paper

Qiqi Xiao, Jiaxu Zou, Muqiao Yang, Alex Gaudio, Kris Kitani, Asim Smailagic, Pedro Costa and Min Xu, Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning, ICIAR, 2019

Please see our Presentation at Conference

Requirements:

pytorch 1.0, torchvision 0.2, numpy 1.16, scikit-learn 0.20, cv2 3.4, PIL 5.1 and ipdb 0.12.

Steps:

1. Download IDRiD dataset and put it under HEDNet_cGAN/data/.

2. To prepare for preprocessing, please run HEDNet_cGAN/blackmask.py to get the mask for each fundus image.

3. For training and evaluating UNet model, please go to UNet/. For training and evaluating HEDNet model, please go to HEDNet/. For training and evaluating HEDNet with conditional GAN model, please go to HEDNet_cGAN/.