Self-supervised based clustering for retinal optical coherence tomography images.

ABSTRACT:

Background

In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented.

Methods

A public dataset was utilized, with 83,484 OCT images with categories of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal fundus. This study employed the Semantic Pseudo Labeling for Image Clustering (SPICE) framework, a self-supervised learning-based method, to cluster unlabeled OCT images into binary and four categories, and the performances were compared with baseline models. We also analysed feature distribution using t-SNE, and explored the cluster centers, attention maps, and misclassified images. In addition, DME and

SUBMITTER: Luo Y 

PROVIDER: S-EPMC11751171 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature