SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images

arXiv:2404.13386v1 Announce Type: cross
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% …

abstract arxiv benefits biomedical cs.cv cs.lg current diagnosis disease disease diagnosis eess.iv evaluation however image images machine machine learning medical power results staff technologies transformer type vision