This project aims to segment 4 common retinal lesions from Fundus Images.
To train a HEDNet model with conditional GAN to segment Hard Exudates using random seed 765, run python train_gan_ex.py --gan True --seed 765.
To train a HEDNet model with conditional GAN to segment Soft Exudates using random seed 765, run python train_gan_se.py --gan True --seed 765.
To train a HEDNet model with conditional GAN to segment Hemorrhages using random seed 765, run python train_gan_he.py --gan True --seed 765.
To train a HEDNet model with conditional GAN to segment Microaneurysms using random seed 765, run python train_gan_ma.py --gan True --seed 765.
When training HEDNet with cGAN, we apply all 3 preprocessing methods (Denoising + Contrast Enhancement + Brightness Balance).
To evaluate the model on the test set, run python evaluate_model.py --seed 765 --lesion 'MA' --model results/models_ma/model.pth.tar for evaluating a saved model checkpoint on MA under results/ using random seed 765. results/models_ma/model.pth.tar is the directory of the saved model checkpoint.
