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.
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.
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.
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.
arXiv:2607. 03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools.
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.
arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.
arXiv:2606. 15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information.
The study examines how the choice of camera—Canon versus Nikon—affects image quality and AI performance in retinal screening for diabetic retinopathy. In a Brazilian cohort of 8,524 patients, Nikon images were more than twice as likely to be of inadequate quality compared with Canon, despite being cheaper. Using a frozen pretrained retinal foundation model, the authors evaluated 12 disease classification tasks across different resolutions and classifiers, finding that camera-related performance differences vary widely and are not consistently mitigated by higher resolution or classifier choice.
arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.
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.
arXiv:2608.22059v1 Announce Type: cross Abstract: Pretrained image encoders are central to medical image classification, where expert annotation is costly and task-specific cohorts are often limited....
MVC-Bench is a new benchmark designed to evaluate the calibration of vision‑language models (VLMs) and medical VLMs (Medical‑VLMs) for medical image classification. It tests calibration across robustness to modality, backbone, and domain shift; effectiveness of calibration strategies and prompt‑tuning methods; and stability under prompt‑template and random‑seed variations. The benchmark includes eight backbones, three medical modalities (fundus imaging, histopathology, chest X‑ray), and compares post‑hoc, train‑time, and zero‑shot calibration approaches, reporting accuracy, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), and Adaptive Calibration Error (ACE) over 1,638 experiments, while also proposing a Multi‑Class Margin (MCM) regularization technique that improves ECE in most settings.