arXiv Computer Vision
Sep 1

retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

arXiv:2602.08580v4 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculat...

By Jose D. Vargas Quiros, Michael J. Beyeler, Sofia Ortin Vela, EyeNED Reading Center, Sven Bergmann, Caroline C. W. Klaver, Bart Liefers, VascX Research Consortium
arXiv Machine Learning
Jul 24

Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.

By Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee
arXiv Computer Vision
Sep 3

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection

The study evaluates fundus-specific foundation models (FM) for detecting diabetic macular edema (DME) in retinal images. It compares two popular FM—RETFound and FLAIR—against a lightweight EfficientNet-B0 backbone across multiple datasets (IDRiD, MESSIDOR-2, and OCT-and-Eye-FundusImages). Results indicate that FM do not consistently outperform fine‑tuned CNNs; EfficientNet-B0 often matches or exceeds FM performance, with FLAIR being the most competitive FM.

By Franco Javier Arellano, Jos\'e Ignacio Orlando
arXiv Machine Learning
Sep 17

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

The paper introduces a biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone to predict diabetic retinopathy stages from OCTA images. This graph-based approach reframes staging as a graph-level classification task solved with a graph neural network, achieving AUC-ROC values up to 84% and outperforming biomarker-based classifiers, CNNs, and vision transformers. The method also provides detailed, interpretable explanations by precisely localizing abnormal vessels and non-perfusion areas.

By Laurin Lux, Alexander H. Berger, Maria Romeo Tricas, Richard Rosen, Alaa E. Fayed, Sobha Sivaprasada, Linus Kreitner, Jonas Weidner, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold