Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
cs.CV, cs.AI
Submitted: 2025-04-22
Updated: 2026-09-08
Code: https://github.com/nkicsl/DDR-dataset
License: http://creativecommons.org/publicdomain/zero/1.0/
The gist: Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their
Terminology
Abstract
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.
Sources
- VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
- On the Opportunities and Risks of Foundation Models
- DINOv2: Learning Robust Visual Features without Supervision
- Hierarchical Text-Conditional Image Generation with CLIP Latents
- FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
- Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Publicly Available Clinical BERT Embeddings
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