  Diabetic Retinopathy (DR) is a leading cause of blindness in working age
adults. DR lesions can be challenging to identify in fundus images, and
automatic DR detection systems can offer strong clinical value. Of the publicly
available labeled datasets for DR, the Indian Diabetic Retinopathy Image
Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of
four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard
exudates. We utilize the HEDNet edge detector to solve a semantic segmentation
task on this dataset, and then propose an end-to-end system for pixel-level
segmentation of DR lesions by incorporating HEDNet into a Conditional
Generative Adversarial Network (cGAN). We design a loss function that adds
adversarial loss to segmentation loss. Our experiments show that the addition
of the adversarial loss improves the lesion segmentation performance over the
baseline.
