Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning

arXiv:2404.13388v1 Announce Type: cross
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 …

abstract accessibility accuracy advantages analysis arxiv blindness cs.cv cs.lg diagnosis diseases eess.iv experts image machine machine learning major medical multiple shortage supervised machine learning type via visual