Efficient Ensemble Learning with Curriculum-Based Masked Autoencoders for Retinal OCT Classification.

ABSTRACT: Background/Objectives: Retinal optical coherence tomography (OCT) is essential for diagnosing ocular diseases, yet developing high-performing multiclass classifiers remains challenging due to limited labeled data and the computational cost of self-supervised pretraining. This study aims to address these limitations by introducing a curriculum-based self-supervised framework to improve representation learning and reduce computational burden for OCT classification. Methods: Two ensemble strategies were developed using progressive masked autoencoder (MAE) pretraining. We refer to this curriculum-based MAE framework as CurriMAE (curriculum-based masked autoencoder). CurriMAE-Soup merges multiple curriculum-aware pretrained checkpoints using weight averaging, producing a single mo

SUBMITTER: Yoon T 

PROVIDER: S-EPMC12840412 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature