Retinal Disease Classification

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36 papers7 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Interpretable Context-Aware Multi-Label Retinal Classification with Dual-Hub Heterogeneous Graph Neural Network
Nov 6, 2026Proshanto Chanda, Munib Mesinovic, Tingting Zhu +1Retinal Disease Classification Multimodal Graph Learning
⋆Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
Within-Family EfficientNet Scaling under Fixed-Resolution Transfer Learning for Spectral-Domain Optical Coherence Tomography Retinal Disease Classification
Nov 4, 2026Shkelqim SherifiRetinal Disease Classification Transfer Learning
University of Tetova
CNN-Based Retinal OCT Classification: Research Gap Analysis and a From-Scratch Baseline, ED126-A
Nov 1, 2026Shkelqim SherifiRetinal Disease Classification Optical Coherence Tomography
Dept. of Computer Science, University of Tetovo, Tetove, R.N.Macedonia
Low-rank MERA Decomposition of 3D OCT Image Volume for Compression, Denoising, Feature Extraction and Diagnosis of Retinal Diseases
Oct 26, 2026Ivica Kopriva, Fei Shi, Marija Štanfel +4Optical Coherence Tomography Medical Image Representation Learning
Ivica Kopriva is with Division of Computing and Data Science, Rudjer Boskovich Institute, Zagreb,Croatia · Fei Shi and Gaowei Li are with the School of Electronics and Information Engineering, Soochow University, Suzhou, China, and the Jiangsu Key Laboratory of Artificial Intelligence and Medical Imaging, Soochow University, Suzhou, China · Marija Štanfel is with Department of Ophthalmology, University Hospital Center Zagreb, Zagreb, Croatia +2
Two for One: Privileged Learning for Resource-Constrained Screening
Oct 21, 2026Lisa Y. W. TangLearning Using Privileged Information Medical Image Analysis
University of British Columbia - Faculty of Medicine
From Canon to Nikon: Camera Domain Shift and Potential Equity Implications in Retinal AI
Oct 21, 2026Lisa Y. W. TangMedical Image Analysis Retinal Disease Classification
University of British Columbia - Faculty of Medicine
Beyond Multi-Stage Pipelines: A Label-Correlation Ensemble for Multi-Label Retinal Fundus Disease Classification
Oct 16, 2026Md Asif Uddin, Jannatul Ferdous, Asif Islam +5Multi-Label Classification Retinal Disease Classification
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
HierarchiRetina: A Diabetic Retinopathy Screening and Grading Cascade with Heterogeneous Mixture-of-Experts Lesion Segmentation
Oct 13, 2026Md Asif Uddin, Niloy Farhan, Rafeed Rahman +1Diabetic Retinopathy Grading Retinal Disease Classification
The authors are with the Department of Computer Science and Engineering, Brac University, Dhaka, Bangladesh
Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models
Oct 9, 2026Niccolò Fineschi, Jovana Panic, Atsushi Watanabe +7Medical Image Representation Learning Medical Imaging Foundation Models
Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy. · Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), The University of Osaka, Osaka, Japan. · D3 Center, The University of Osaka, Osaka, Japan. +2
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
Explainable Diabetic Retinopathy Classification Using Vision Foundation Models
Aug 28, 2026Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar +1Retinal Disease Classification Medical Image Classification
Department of Health Services Research, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification
Aug 27, 2026Bekzat Nurlanbekova, Fung Fung TingRetinal Disease Classification Medical Image Representation Learning
∗† School of Information Technology Monash University Malaysia, Malaysia
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
RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Feature-Adaptive Mamba Projection
Aug 18, 2026Cheng Cheng, Jin HongRetinal Disease Classification Medical Image Classification
School of Artificial Intelligence, Nanchang University, Nanchang 330031, China · School of Information Engineering, Nanchang University, Nanchang 330031, China
Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading
Aug 15, 2026Saksham KumarDiabetic Retinopathy Grading Retinal Disease Classification
Department of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, Bihar, 801106, India
TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification
Aug 14, 2026Md Ashraful Hossen Akash, Shyla Afroge, Abdullah Al Mamun +2Medical Image Classification Retinal Disease Classification
Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi-6204, Bangladesh · Elite Research Lab LLC, New York, USA · Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia +1
Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing
Aug 10, 2026Nagur Shareef Shaik, Jeongwoo Park, Yeong-Jin Kim +3Medical Imaging Sparse Mixture-of-Experts
Department of Computer Science, Georgia State University, Atlanta, GA 30324, USA · Piehealthcare Inc., Suwon-si, Gyeonggi-do, 16311, Republic of Korea
PARAGraph: Pathology-Anatomy-Aware Hierarchical Graph for Diabetic Retinopathy Grading
Aug 8, 2026Ziyang Zhang, Yuankai Huo, Yalin Zheng +1Graph Neural Networks Diabetic Retinopathy Grading
Vanderbilt University, USA · University of Liverpool, UK
Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification
Jul 23, 2026Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya ChatterjeeCounterfactual Explanations Medical Image Classification
Department of Mechanical Engineering, National Institute of Technology Durgapur, West Bengal, 713209, India · Department of Electrical Engineering, IIT Kanpur, Uttar Pradesh, 208016, India · Department of Electrical Engineering, National Institute of Technology Durgapur, West Bengal, 713209, India
Calibrated Hybrid CNN-Transformer for Retinal OCT Classification
Jul 9, 2026Animesh KumarUncertainty Quantification Optical Coherence Tomography
School of Computing, Newcastle University, UK
Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy
Jul 6, 2026Inam Ullah, Imran Razzak, Shoaib JameelCausal Mediation Analysis Diabetic Retinopathy Grading
University of Southampton Southampton, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates
A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading
Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelGraph Representation Learning Diabetic Retinopathy Grading
University of Southampton Southampton, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Evo-RAD: Navigating Rare Retinal Disease Diagnosis via Self-Evolving Agentic Retrieval
Jun 22, 2026Wangding Xia, Ye Du, Jiashi Lin +3Medical Image Retrieval Reinforcement Learning
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · School of Computer Science, Northwestern Polytechnical University, Xi’an, China · Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore +3
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
Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
May 25, 2026Durjoy Dey, Aymane Ajbar, Yuhong YanMedical Image Classification Vision Transformer
Department of Computer Science and Software Engineering Concordia University Montreal, QC, Canada · Ebovir Biotechnologie Inc. Montreal, QC, Canada
Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis
May 13, 2026Yingzhe Ma, Xiao Yang, Yuguo Yin +1Explainable Medical Image Analysis Medical Image Analysis
University of Electronic Science and Technology of China · Peking University
Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation
May 7, 2026Abdelrahman Zaian, Sheethal Bhat, Mohamed Abdalkader +1Diabetic Retinopathy Grading Medical Report Generation
Friedrich-Alexander-Universität, Germany
Toward Multimodal Conversational AI for Age-Related Macular Degeneration
Apr 28, 2026Ran Gu, Benjamin Hou, Mélanie Hébert +5Medical Diagnosis Medical Visual Question Answering
Division of Intramural Research, NaMonal Library of Medicine, NaMonal InsMtutes of Health, Bethesda, Maryland, USA · 2. Division of Epidemiology and Clinical ApplicaMons, NaMonal Eye InsMtute, NaMonal InsMtutes of Health, Bethesda, Maryland, USA · 1. Division of Intramural Research, NaMonal Library of Medicine, NaMonal InsMtutes of Health, Bethesda, Maryland, USA
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
Apr 25, 2026Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba +2Diabetic Retinopathy Grading Deep Ensembles
Department of Informatics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Salerno, Italy · Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology, Gjøvik, 2815, Norway
Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration
Apr 14, 2026Chi Liu, Yuzhuo Zhou, Sheng Shen +9Glaucoma Detection Medical Image Analysis
Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira, Taipa, 999078, Macao, China · State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510000, Guangdong, China · Hainan International College, Minzu University of China, Li’an International Education Innovation Pilot Zone, Lingshui, 572423, Hainan, China +4

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Interpretable Context-Aware Multi-Label Retinal Classification with Dual-Hub Heterogeneous Graph Neural Network

Proshanto Chanda, Munib Mesinovic, Tingting Zhu, Abhirup Banerjee

⋆Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK

Retinal Disease Classification Multimodal Graph Learning Heterogeneous GNNs Multimodal Model Interpretability

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Multi-label retinal screening requires capturing co-occurring pathologies and clinical context, yet standard multimodal fusion remains opaque. We propose a dual-hub heterogeneous graph neural network (GNN) that couples spatial fundus features and structured clinical covariates through cross-modal message passing while providing inherent interpretability. Rather than relying on post-hoc saliency approximations, our architecture exposes multi-level decision pathways during forward aggregation: spatially biased self-attention captures lesion-level patch interactions, while modality-specific graph attention coefficients explicitly quantify the contribution of visual regions and clinical risk factors to each disease node. Evaluated on the BRSET dataset (16,266 photographs from 8,524 patients), the proposed model achieves 76.1% Macro-AUPRC across seven conditions, matching or exceeding image-only (75.9%) and ResNet-50+MLP fusion (75.9%) baselines, with largest contextual gains in macular edema (+3.5%) and diabetic retinopathy (+2.1%). Codebase is available at: https://github.com/proshanto-c/ca-mlc-refactor.