Abstract
Diabetic retinopathy (DR) is an eye disease associated with diabetes mellitus that affects retinal blood vessels. Early detection is crucial to prevent vision loss. The most common method for detecting the disease is the analysis of digital fundus images, which show lesions of small vessels and functional abnormalities.
Manual detection and segmentation of lesions is a time-consuming task requiring proficient skills. Automatic methods for retinal image analysis could help ophthalmologists in large-scale screening programs of population with diabetes mellitus allowing cost-effective and accurate diagnosis.
In this work we propose a fully convolutional neural network with adversarial training to automatically segment DR lesions in funduscopy images.
