DINO-EYE: self-supervised learning for identification of different optic disc phenotypes in primary open angle glaucoma.

ABSTRACT: To develop a self-supervised learning (SSL) model that classifies optic disc phenotypes in primary open angle glaucoma (POAG) and explores novel phenotypic patterns with optic disc photographs (ODPs). We collected 850 ODPs from patients with POAG and applied data augmentation to address class imbalances, yielding 10,493 images. Using the DINO Vision Transformer as the backbone model, we trained an SSL model to extract 2048-dimensional latent features. These features were used for both supervised classification of six known phenotypes and unsupervised clustering. Classification performance was evaluated with Random Forest and XGBoost models. UMAP (Uniform Manifold Approximation and Projection) was used for dimensionality reduction and feature visualization, and attention maps were generated

SUBMITTER: Grassi L 

PROVIDER: S-EPMC12830587 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature