MIRAGE: Multimodal foundation model and benchmark for comprehensive
  retinal OCT image analysis
Authors: name: José Morano, name: Botond Fazekas, name: Emese Sükei, name: Ronald Fecso, name: Taha Emre, name: Markus Gumpinger, name: Georg Faustmann, name: Marzieh Oghbaie, name: Ursula Schmidt-Erfurth, name: Hrvoje Bogunović

Artificial intelligence (AI) has become a fundamental tool for assisting
clinicians in analyzing ophthalmic images, such as optical coherence tomography
(OCT). However, developing AI models often requires extensive annotation, and
existing models tend to underperform on independent, unseen data. Foundation
models (FMs), large AI models trained on vast unlabeled datasets, have shown
promise in overcoming these challenges. Nonetheless, available FMs for
ophthalmology lack extensive validation, especially for segmentation tasks, and
focus on a single imaging modality. In this context, we propose MIRAGE, a novel
multimodal FM for the analysis of OCT and scanning laser ophthalmoscopy (SLO)
images. Additionally, we propose a new evaluation benchmark with OCT/SLO
classification and segmentation tasks. The comparison with general and
specialized FMs and segmentation methods shows the superiority of MIRAGE in
both types of tasks, highlighting its suitability as a basis for the
development of robust AI systems for retinal OCT image analysis. Both MIRAGE
and the evaluation benchmark are publicly available:
https://github.com/j-morano/MIRAGE.