Fairer AI in Ophthalmology via Implicit Fairness Learning for Mitigating Sexism and Ageism

We collect the largest and most diverse fundus image dataset with data from over 8,405 patients representing a wide age range (0 to 90 years). The fundus dataset contains two types of advanced ultra-widefield and regular narrow-angle fundus images, with the ultra-widefield imaging dataset containing 16,530 fundus images annotated with 38 ophthalmic diseases and 67 fundus features and the narrow-angle imaging dataset containing 4,540 fundus images annotated with 16 ophthalmic diseases and 20 fundus features. We hereby only provide the test set of our dataset, while the complete dataset can be accessed through the Google Drive link provided in the paper. (Despite our attempts to upload the full dataset, the transmission time was excessively long due to its size exceeding 100G, necessitating this alternative method.)