SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
This method addresses the challenge of expanding effective databases for eye disease diagnosis in resource-limited regions, though it is incremental as it applies self-supervised learning to a specific domain.
The authors tackled the problem of heavy labeling workload in supervised fundus image diagnosis by proposing SSVT, a self-supervised vision transformer that achieves 97.0% accuracy in diagnosing four main eye diseases using unlabeled images from public and hospital datasets.
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.