Eye Disease Diagnosis and Fundus Synthesis: A Large-Scale Dataset and Benchmark

As one of the most common imaging modalities, retinal fundus imaging offers images of interior surface of eyes for initial examination of disorders. Data-driven machine learning methods, especially deep learning models in recent years, provide automatic ophthalmological disease diagnosis techniques from color fundus images. Data with high quality, diversity and balanced distribution supports deep model-based eye disease diagnosis. However, many existing datasets focus on a specific kind of eye disease, and some suffer from label noise or quality degeneration, which hinders automatic screening algorithms from dealing with multiple eye diseases. To solve this, we propose a high-quality dataset containing 28877 color fundus images for deep learning-based diagnosis. Except for 15000 healthy samples, the dataset consists of 8 eye disorders including diabetic retinopathy, age-related macular degeneration, glaucoma, pathological myopia, hypertension, retinal vein occlusion, LASIK spot and others. Based on this, we propose a co-attention network for disease diagnosis, establish benchmark on screening and grading tasks, and demonstrate that the proposed dataset supports generative adversarial network-based image synthesis. The dataset will be made publicly available.