  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.
