Abstract
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervised methods, bringing in a heavy workload to biomedical staff and hence suffering in expanding effective databases. To address this issue, in this article, we established a label-free method, name 'SSVT',which can automatically analyze un-labeled fundus images and generate high evaluation accuracy of 97.0% of four main eye diseases based on six public datasets and two datasets collected by Beijing Tongren Hospital. The promising results showcased the effectiveness of the proposed unsupervised learning method, and the strong application potential in biomedical resource shortage regions to improve global eye health.
Abstract (translated)
基于机器学习的 fundus 图像诊断技术因其节省医疗资源、提供客观评估结果等好处而引起了全球关注。然而，现有的方法通常基于监督方法，给生物医学工作人员带来繁重的工作负担，导致难以扩展有效的数据库。为了应对这个问题，本文我们建立了一种名为 'SSVT' 的无标签方法，通过自动分析未标注的 fundus 图像并基于六个公开数据集和北京同仁医院的两个数据集，可以生成四种主要眼病97.0%的高评估精度。这种无标签学习方法展示出所提出的自监督学习方法的效力，以及在生物资源短缺地区改善全球眼健康具有强大的应用潜力。
URL
https://arxiv.org/abs/2404.13386
