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.
Abstract (translated)
翻译： fundus diseases是全球导致视力残疾和盲的主要原因，特别是在欠发达地区，由于眼科医生的短缺，导致及时诊断遇到困难。 AI辅助 fundus 图像分析具有 several 优势，如高准确度、减轻的工作负担和提高的可访问性，但建立可靠的模型需要大量专家注释的数据。为解决这一困境，我们提出了一个通用的自监督机器学习框架，可以处理未标注的 fundus 图像中的各种 fundus diseases。我们方法 的 AUC 超过了现有监督方法的 15.7%，甚至超过了单个人类专家的性能。此外，我们的模型对各种数据集（地区、种族和多相机或设备中的图像来源或质量）适应性良好。我们的方法为无标签的 fundus disease 诊断提供了一个通用的框架，这有可能潜在地改善针对视力即将丧失的人群的远程医疗程序的早期筛查。
URL
https://arxiv.org/abs/2404.13388
