SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
<|reference_start|>SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images: 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.<|reference_end|>
@article{wang2024ssvt:,
    title={SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based
  On Fundus Images},
    author={Jiaqi Wang, Mengtian Kang, Yong Liu, Chi Zhang, Ying Liu, Shiming Li,
  Yue Qi, Wenjun Xu, Chenyu Tang, Edoardo Occhipinti, Mayinuer Yusufu, Ningli
  Wang, Weiling Bai, Shuo Gao, Luigi G. Occhipinti},
    journal={arXiv preprint arXiv:2404.13386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13386},
    primaryClass={eess.IV cs.CV cs.LG}
}