RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation
  Models
Authors: name: Ronald Fecso, name: José Morano, name: Ursula Schmidt-Erfurth, name: Hrvoje Bogunović

The rise of imaging techniques such as optical coherence tomography (OCT) and
advances in deep learning (DL) have enabled clinicians and researchers to
streamline retinal disease staging. A popular DL approach is self-supervised
learning (SSL), where models learn from vast amounts of unlabeled data,
avoiding costly annotation. SSL has allowed the development of foundation
models (FMs), large models that can be used for a variety of downstream tasks.
However, existing FMs for OCT, trained solely on image data, lack a
comprehensive and robust semantic understanding of images, as evidenced by
their downstream performance (especially for complex tasks), and thus require
supervised fine-tuning (which may be unfeasible) to better adapt to specific
applications and populations. To address this, we propose RetFiner, an SSL
vision-language refinement scheme that improves the representations of existing
FMs and enables their efficient and direct adaptation to specific populations
for improved downstream performance. Our method uses a diverse set of training
objectives which take advantage of the rich supervisory signal found in textual
data. We tested RetFiner on the retinal FMs RETFound, UrFound, and VisionFM,
showing significant improvements in linear probing performance on seven highly
diverse OCT classification tasks, with an average increase of 5.8, 3.9, and 2.1
percentage points over their baselines, respectively. Our code and model
weights are publicly available at https://github.com/ronnief1/RetFiner.