Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
The advent of foundation models (FMs) is transforming medical domain. In
ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4
million natural images and 1.6 million retinal images, has demonstrated high
adaptability across clinical applications. Conversely, DINOv2, a
general-purpose vision FM pre-trained on 142 million natural images, has shown
promise in non-medical domains. However, its applicability to clinical tasks
remains underexplored. To address this, we conducted head-to-head evaluations
by fine-tuning RETFound and three DINOv2 models (large, base, small) for ocular
disease detection and systemic disease prediction tasks, across eight
standardized open-source ocular datasets, as well as the Moorfields AlzEye and
the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting
diabetic retinopathy (AUROC=0.850-0.952 vs 0.823-0.944, across three datasets,
all P<=0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P<0.001). In
glaucoma, DINOv2-base model outperformed RETFound (AUROC=0.958 vs 0.940,
P<0.001). Conversely, RETFound achieved superior performance over all DINOv2
models in predicting heart failure, myocardial infarction, and ischaemic stroke
(AUROC=0.732-0.796 vs 0.663-0.771, all P<0.001). These trends persisted even
with 10% of the fine-tuning data. These findings showcase the distinct
scenarios where general-purpose and domain-specific FMs excel, highlighting the
importance of aligning FM selection with task-specific requirements to optimise
clinical performance.
