The paper presents an explainable diabetic retinopathy classification framework that leverages vision foundation models—DINOv2, CLIP, and Vision Transformer—combined with various transfer learning techniques such as full fine‑tuning, linear probing, and Low‑Rank Adaptation (LoRA). Using the ODIR dataset for internal validation and the APTOS dataset for external testing, DINOv2‑LoRA achieved the best internal AUROC (0.758) while DINOv2 and ViT full fine‑tuning reached the highest external AUROC (0.920). Explainability was assessed with Grad‑CAM and HiResCAM against expert‑annotated lesion masks from IDRiD, using Dice, IoU, and Pointing Game metrics, confirming that model attention aligns with clinically relevant retinal lesions.
By Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar, Juan Miguel Lopez Alcaraz
CF‑NODE is a counterfactual test‑time adaptation framework designed for diabetic retinopathy grading across different cameras, clinics, and patient populations. It ranks internal spatial nodes by attention, feature magnitude, and similarity, then progressively attenuates the highest‑ranked nodes and calibrates predictions based on the stability of the expected grade response. Evaluated on five external datasets, CF‑NODE outperforms four existing methods, achieving higher AUC and macro‑F1 scores and providing more balanced recognition across severity levels.
By Sabarish G, Deepak Parashar
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
By David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante
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:2609.23561v1 Announce Type: new
Abstract: Deploying efficient neural networks is essential in resource-constrained environments, yet compact models often sacrifice interpretability - a critical...
By Aleks Czufarow, Ihor Babin
The study evaluates offline response-based knowledge distillation for five-class diabetic retinopathy grading on the APTOS 2019 dataset. Five teacher architectures—ResNet‑50, MobileNetV3‑Large, and Swin Transformer‑Tiny—were distilled into a MobileNetV3‑Small student, exploring a grid of teacher, temperature, and hard‑label weight settings. The best configuration achieved a quadratic weighted kappa of 0.9093 (macro‑F1 0.6851) on a single seed, with an average kappa of 0.9024 across three seeds, while reducing parameters by 18.07× compared to the Swin‑T teacher.
By Mossa Manisa, Habiba Jahan Priya, A. K. M. Fakhrul Hossain