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: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
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
arXiv:2607. 02569v1 Announce Type: cross Abstract: This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using RETFound, a self-supervised vision-transformer retinal foundation model used here as a frozen feature encoder, and the public APTOS 2019 and DDR diabetic retinopathy fundus image datasets.
By Karim Mardhani
arXiv:2608.24723v1 Announce Type: new
Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent re...
By Xiaoyan Li, Shixin Xu, Arvind Gupta, Huaxiong Huang
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretabilit...
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 study presents an unsupervised domain adaptation model that classifies normal versus abnormal retinal development using optical coherence tomography (OCT) scans from multiple devices and centres. Trained on labelled data from one OCT system and unlabelled data from another, the ResNet50‑based Domain Adversarial Neural Network achieved accuracies between 87.75% and 96.06% across six source‑to‑target pairings and maintained performance on an unseen Heidelberg Spectralis dataset. The approach demonstrates that OCT classification can be transferred across devices without requiring labelled target data, potentially enabling more consistent assessment of retinal development and large‑scale analysis of existing imaging datasets.
By Zhanhan Tu, Nikhil Reddy, Helen J. Kuht, Ha-Jun Yoon, Gail D. E. Maconachie, Ravi Purohit, Prateek Pani, Line Kessel, Jinu Han, Jing Jin, Shui-Hua Wang, Karthik Bharadwaj, Viral Sheth, Michael Hisaund, Rebecca J. McLean, George Garratt, Chetan Kantibhai Patel, Kanmin Xue, Callum Hunt, Zisen Zhou, Ian De Silva, Seema Teli, Sonal Shah, Vanessa Rodwell, Aarti Patel, Irene Gottlob, Yu-Dong Zhang, Zedong Zheng, Girish Varma, Mervyn G. Thomas
arXiv:2608. 09752v1 Announce Type: cross Abstract: Retinal fundus images frequently exhibit multiple co-occurring pathologies, yet standard deep learning classifiers apply static, identical computation to every image regardless of the underlying disease distribution.
By Nagur Shareef Shaik, Jeongwoo Park, Yeong-Jin Kim, Jaeuk Jung, Hyunjung Oh, Dong Hye Ye
The paper introduces RetinaGroundBench, a new benchmark for visual grounding of retinal OCT images, featuring 815 B-scans annotated with 4,537 bounding boxes for 13 biomarkers across 8 diseases. It evaluates 27 multimodal large language models (MLLMs) and finds that visual grounding is largely unsolved, with the best model achieving only a macro F1 of 23.7%. The authors also present RetGrounder, an 8‑B parameter MLLM fine‑tuned for OCT, which raises grounding F1 to 33.9%, a 43% relative improvement.
By Ronald Fecso, Jos\'e Morano, Botond Fazekas, Paul Widmann-Sedlnitzky, Dorota Himel, Laetita Hinterhuber, Virginia Mares, Andreas Pollreisz, Hrvoje Bogunovi\'c
Co-Annotator is a clinical AI system that distills expert gaze and dictation into two guidance components: a gaze‑aligned Vision Transformer that highlights fixation‑aligned areas of interest (AOIs) and an ontology‑bounded vision‑language model that pre‑fills editable biomarker summaries for retinal OCT. In controlled studies, each modality independently improved diagnostic accuracy and biomarker generation, and when combined across two academic institutions, the system increased correct diagnoses per minute by 40% and reduced comment editing time by 67% without compromising accuracy.
By Ziheng "Leo" Li, Benjamin Freeman, Akshay Raman, Kavin Aravindhan Rajkumar, Xinxin Fang, Rishabh Srivastava, Steven Feiner, Kaveri A. Thakoor
Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology they report rather than...