Ophthalmic Image Analysis

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

CAMEL: Cross-Architecture Masked Embedding Learning for Ultra-Lightweight Retinal Foundation Models
Oct 29, 2026Andre Guarnier De Mitri, Giovani Decico Lucafo, Miller Matheus Lima Anacleto Rocha +1Medical Imaging Foundation Models Cross-Architecture Knowledge Distillation
ICMC, University of São Paulo São Carlos, São Paulo, Brazil
Compression of Vision Foundation Models for Retinal OCT via Growing-Neural-Gas Codebooks and Graph-Guided Masked Image Modeling
Oct 23, 2026Ali Yavari, Tilman Schmoll, Rainer A. Leitgeb +2Model Compression Vision Foundation Models
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, Vienna, 1090, Austria · Carl Zeiss Meditec AG, Rudolf-Eber-Straße 11, Oberkochen, 73447, Germany
Augmentation Strategy for DINO in Glaucoma Classification
Oct 23, 2026Ashish Kumar Meena, Chandra Sekhar SeelamantulaSelf-Supervised Learning Self-Supervised Visual Representation Learning
Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
Evidence-Guided Selection of Retinal Vessel Segmentation Pipelines: A Cross-Dataset Benchmark of Performance, Structure, Failure Modes, and Reproducibility
Oct 22, 2026Ruoyu Yan, Yuejia Liu, Shixin Xu +4Retinal Vessel Segmentation Ophthalmic Image Analysis
Duke Kunshan University, Kunshan, China · University of Toronto, Toronto, Canada · Beijing Normal–Hong Kong Baptist University, Zhuhai, China +1
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 +3Parameter Isolation Ophthalmic Image Analysis
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 Visual Question Answering
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
Polar-domain Segmentation of Peripapillary Atrophy and Optic Disc
Aug 9, 2026Mengxian He, Yunyun Sun, Ziyue Gao +3Medical Image Segmentation Medical Image Analysis
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 +23Fundus Imaging 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

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CAMEL: Cross-Architecture Masked Embedding Learning for Ultra-Lightweight Retinal Foundation Models

Andre Guarnier De Mitri, Giovani Decico Lucafo, Miller Matheus Lima Anacleto Rocha, Diego Furtado Silva

ICMC, University of São Paulo São Carlos, São Paulo, Brazil

Medical Imaging Foundation Models Cross-Architecture Knowledge Distillation Model Compression Ophthalmic Image Analysis

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Recent progress in deep learning has substantially advanced automated grading of ophthalmologic pathologies, supported by large annotated datasets and improved representation learning. However, a significant gap remains between state-of-the-art performance and clinical scalability. In particular, foundation models have reshaped image-based medical screening and diagnosis. These large-scale architectures trained on extensive datasets learn rich, generalizable feature representations that can be adapted to a wide range of downstream tasks. In ophthalmology, RETFound, one of the most relevant foundation models for retinal images, performs strongly across classification, segmentation, and risk prediction tasks. Despite their effectiveness, foundation models are often computationally prohibitive, hindering their deployment in real-world clinical environments where hardware constraints and latency are critical. To bridge this gap, we introduce CAMEL (Cross-Architecture Masked Embedding Learning), a novel cross-architecture distillation framework that uses an asymmetric masking strategy during embedding learning to effectively transfer knowledge from massive teacher foundation models to compact, lightweight student models. Our experimental evaluation across four datasets shows that CAMEL preserves accuracy while drastically reducing computational complexity, enabling lightweight yet highly accurate models suitable for resource-limited clinical settings.