  Fundus diseases are major causes of visual impairment and blindness
worldwide, especially in underdeveloped regions, where the shortage of
ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis
has several advantages, such as high accuracy, reduced workload, and improved
accessibility, but it requires a large amount of expert-annotated data to build
reliable models. To address this dilemma, we propose a general self-supervised
machine learning framework that can handle diverse fundus diseases from
unlabeled fundus images. Our method's AUC surpasses existing supervised
approaches by 15.7%, and even exceeds performance of a single human expert.
Furthermore, our model adapts well to various datasets from different regions,
races, and heterogeneous image sources or qualities from multiple cameras or
devices. Our method offers a label-free general framework to diagnose fundus
diseases, which could potentially benefit telehealth programs for early
screening of people at risk of vision loss.
