Ophthalmic Image Analysis

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42 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

CF-NODE: Counterfactual Spatial Node Intervention for Test-Time Adaptation Under Domain Shift, Evaluated Across Five Diabetic Retinopathy Datasets
Oct 15, 2026Sabarish G, Deepak ParasharTest-Time Adaptation Diabetic Retinopathy Grading
FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs
Sep 27, 2026David Restrepo, Chenwei Wu, Luis Filipe Nakayama +4Fairness in Medical Image Classification Ophthalmic Image Analysis
CentraleSupélec, Université Paris-Saclay, France · Cancer Data Science Unit, IHU PRISM, Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, France · University of Michigan, USA +4
Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning
Sep 26, 2026Nanxi Yu, Kang Li, Ye Du +3Ophthalmic Image Analysis Domain-Incremental Learning
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China · Department of Biomedical Engineering and Sports Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China · School of Future Technology, South China University of Technology, Guangdong, China +1
EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding
Sep 26, 2026Gujie Shao, Zixun Xie, Xuechun Xing +7VLM Evaluation Medical VQA
Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa
Sep 22, 2026Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye +4Fundus Imaging Medical Diagnosis
Department of Ophthalmology, Emory University, Atlanta, Georgia, United States · Department of Biomedical Engineering, National University of Singapore, Singapore · Singapore Eye Research Institute, Singapore National Eye Centre, Singapore +5
Multimodal Shared Latent Representation of Narration, Microscope and iOCT Images for Phase Recognition in Vitreoretinal Surgery
Aug 31, 2026Onur Izmitlioglu, Shervin Dehghani, Tarek Ghannoum +2Surgical Video Understanding Multimodal Contrastive Learning
Computer Aided Medical Procedures, Technical University of Munich, Germany · SynthesEyes GmbH, Munich, Germany · Department of Ophthalmology, LMU University Hospital, Munich, Germany
Co-Annotator: Expert-Distilled ViT and VLM for Visual and Documentation Guidance in Age-Related Macular Degeneration
Aug 31, 2026Ziheng "Leo" Li, Benjamin Freeman, Akshay Raman +5Medical Report Generation Medical Image Analysis
Columbia University
Automated 2D and 3D Segmentation of AMD and DME Lesions in OCT
Aug 27, 2026Lucia Sundberg, Zhihao Zhao, M. Ali NasseriOphthalmic Image Analysis Medical Image Segmentation
MAPS Lab, Technical University of Munich, Munich, Germany. · University of Wisconsin-Madison, Madison, Wisconsin, USA.
Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features
Aug 25, 2026Xiaoyan Li, Shixin Xu, Arvind Gupta +1Ophthalmic Image Analysis Medical Image Classification
Computer Science, University of Toronto, 27 King’s College Cir., Toronto, M5S 1A1, Ontario, Canada. · Zu Chongzhi Center, Duke Kunshan University, No.8 Duke Ave., Kunshan, 215300, Jiangsu, China. · Mathematics and Statistics, York University, 4700 Keele St., Toronto, M3J 1P3, Ontario, Canada. +1
OptiModNet: A UNet-Transformer Hybrid with Grouped-Query and Channel Attention for Optic Disc and Cup Segmentation
Aug 19, 2026Soumili Ghosh, Debapriya Roy, Aryan Das +1Optic Disc Segmentation Ophthalmic Image Analysis
IEM Saltlake,India · Techno Main Salt Lake, India · VIT Bhopal University, India +1
Optic Disc Segmentation in Fundus Images: From Classical Image Processing and Deformable Models to Modern AI
Aug 18, 2026Buket D. BarkanaOptic Disc Segmentation Ophthalmic Image Analysis
Biomedical Engineering Department, The University of Akron, Akron, OH 44325, USA
Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis
Aug 15, 2026Lovre Antonio Budimir, Mingya Alexa Gong, Alyssa Foong Quinney +5Ophthalmic Image Analysis Medical Imaging Foundation Models
Faculty of Electrical Engineering and Computing, University of Zagreb, HR · Medical Physics and Biomedical Engineering, University College London, UK · Institute of Ophthalmology, University College London, UK +4
UPolarSQ: Polar Representation Learning for Optic Disc and Peripapillary Atrophy Segmentation and Quantification in Fundus Photographs
Aug 9, 2026Mengxian He, Yunyun sun, Ziyue Gao +3Medical Image Analysis Medical Image Segmentation
Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · Capital Medical University, Beijing, China
An Agentic AI Framework Overcomes Fundamental Limitations of Large Language Models for Glaucoma Detection from Fundus Photography
Aug 7, 2026Jalil Jalili, Hossein Taghizad, Anuwat Jiravarnsirikul +8Tool-Using Vision-Language Agents Glaucoma Detection
Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. · Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA · Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivières, Trois-Rivières, QC, Canada +2
SpecF2M: A Spectral-Aware Multi-task Network Estimating Axial Length and Refractive Error from Pediatric Fundus Photographs
Aug 7, 2026Mengxian He, Xinyue Liu, Yunyun Sun +5Medical Imaging Multi-Task Learning
The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong · Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China · Capital Medical University, China +1
Algorithmic statistics of retinal images
Aug 6, 2026Loan Huynh, Ronald Zambrano, Layton Aho +4Medical Imaging Longitudinal Medical Image Analysis
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA · Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA · School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA +2
ReMoE: Report-Guided Mixture-of-Experts for Multimodal OCT/OCTA Anomaly Detection
Jul 31, 2026Zihan Nie, Qincheng Qiao, Muhao Xu +4Mixture of Experts Optical Coherence Tomography
School of Mechanical Engineering, Shandong University, Jinan, China · 2Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan, China · 6Airdoc-Monash Research, Monash University, Clayton, VIC 3800, Australia +3
From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification
Jul 20, 2026K. Mithra, Prem Kumar SanthanamMedical Image Classification Automated Grading
Independent Researcher, Information and Communication Engineering, Scottsdale, AZ, USA · Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, USA
Can Multimodal Large Language Models Understand OCT?
Jul 18, 2026Baochen Fu, Wenzhi Deng, Baihao Jin +5Medical Image Benchmarks Medical Imaging
1Shandong University · 4Shandong Provincial Qianfoshan Hospital · University of Science and Technology of China +2
Uncertainty-Aware Multi-Source Retinal Fluid Segmentation in OCT
Jul 9, 2026Animesh KumarDomain Adaptation Medical Imaging
Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
IRIS: An Intelligent Vision-Language System for Ocular Surface Diseases via Topic Tree and Scene-Driven VQA Generation
Jul 5, 2026Hao Wei, Wenjin Qi, Dasen Dai +2Visual Question Answering Vision-Language Models
The Chinese University of Hong Kong, Shatin, Hong Kong, China · Westlake University, Xihu District, Hangzhou, Zhejiang Province, China
Fully Automated High-Precision Segmentation of Retinal Atrophy and Ellipsoid Zone Thickness in OCT: A Reliable Tool for Real-World GA Monitoring
Jun 30, 2026Wolf-Dieter Vogl, Hlynur Skulason, Oliver Leingang +3Medical Imaging Optical Coherence Tomography
RetInSight GmbH, Vienna, Austria · Laboratory of Opthalmic Image Analysis (OPTIMA), Medical University of Vienna, Austria
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Jun 24, 2026Pengwei Wang, José Morano, Virginia Mares +1Fundus Imaging Explainable Medical Image Analysis
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria · Christian Doppler Lab for Artificial Intelligence in Retina, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Jun 18, 2026Pengwei Wang, José Morano, Qian Wan +1Fundus Imaging Explainable Medical Image Analysis
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria
Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
Jun 17, 2026Veit Hucke, Thomas Pinetz, Gregor Reiter +2Image Denoising Medical Imaging
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Austria · Department of Ophthalmology and Optometry, Medical University of Vienna, Austria +1
EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining
Jun 13, 2026Zhuo Deng, Ruiheng Zhang, Ziheng Zhang +23Medical Imaging Foundation Models Cross-Modal Representation Learning
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China · Liangxiang Hospital of Beijing Fangshan District, Capital Medical University, Beijing, China +6
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
Jun 3, 2026Yinxiang Yu, Maoxiang Chu, Qi Niu +7Medical Imaging Ophthalmic Image Analysis
School of Electronic and Information Engineering, University of Science and Technology Liaoning, No.189 Qianshan Middle Road, Lishan District, Anshan, 114051, Liaoning, China
Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis
May 13, 2026Yingzhe Ma, Xiao Yang, Yuguo Yin +1Retinal Disease Classification Medical Image Analysis
University of Electronic Science and Technology of China · Peking University
Scientific Domain Knowledge Improves Vision-Language Fundus Models
May 4, 2026Verena Jasmin Hallitschke, Carsten Eickhoff, Philipp BerensMedical VLMs Healthcare
1Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Institute for Applied Medical Informatics, University of Tübingen, Tübingen, Germany · 2Tübingen AI Center, Tübingen, Germany
Representation learning from OCT images
May 4, 2026Hedi Tabia, Désiré Sidibé, Nawres Khlifa +6Multimodal Learning Medical Imaging Foundation Models
IBISC Univ. Evry Université Paris-Saclay · University of Tunis El Manar · ESIEE Paris - Université Gustave Eiffel +2

Oct 15, 2026J/K move · Enter open · S save

CF-NODE: Counterfactual Spatial Node Intervention for Test-Time Adaptation Under Domain Shift, Evaluated Across Five Diabetic Retinopathy Datasets

Sabarish G, Deepak Parashar

Test-Time Adaptation Diabetic Retinopathy Grading Ophthalmic Image Analysis

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Automated fundus-based diabetic retinopathy (DR) grading can substantially expand screening capacity, but source-trained models degrade sharply across different cameras, clinics, and patient populations. Existing test-time adaptation methods depend on entropy minimization or target information accumulated from image batches and streams. These mechanisms can reinforce confidently incorrect predictions and break down when clinical images arrive independently. We introduce CF-NODE, a novel counterfactual test-time adaptation framework for cross- domain DR grading. Instead of trusting prediction confidence or estimating a target distribution, CF-NODE asks how each prediction responds under a controlled intervention on the evidence supporting it. CF-NODE ranks internal spatial nodes using model-derived attention, feature magnitude, and node-set similarity, progressively attenuates the highest-ranked nodes, and uses the stability of the resulting expected-grade response to apply a bounded calibration of the predicted grade distribution. The framework is fully frozen and adapts every image independently using only geometric views and a fixed source-derived prior, requiring no target-derived information or model updates. We evaluate CF-NODE on five external DR datasets under repeated-seed evaluation against TENT, EATA, SAR, and T3A. CF-NODE outperforms all four methods with statistically significant improvements over the second-best method, improves AUC on every target domain, and attains the highest average AUC and macro-F1, delivering stronger grade discrimination together with more balanced recognition across DR severity levels. These results demonstrate that counterfactual intervention sensitivity provides consistent and deployment-ready domain robustness for single-image clinical inference.