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Electrical Engineering and Systems Science > Image and Video Processing
Title: Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
(Submitted on 20 Apr 2024 (v1), last revised 23 Apr 2024 (this version, v2))
Abstract: 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.
Submission history
From: Chenyu Tang [view email][v1] Sat, 20 Apr 2024 14:15:25 GMT (4006kb)
[v2] Tue, 23 Apr 2024 13:25:01 GMT (2813kb)
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