Retinal Disease Classification

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30 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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 +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
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 +1Retinal Disease Classification 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 +1Multimodal RAG 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 VQA
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
Retinal Malady Classification using AI: A novel ViT-SVM combination architecture
Mar 31, 2026Shashwat Jha, Vishvaditya Luhach, Raju PoddarRetinal Disease Classification Medical Image Classification
Department of Electrical and Electronics Engineering, Birla Institute of Technology Mesra · Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology · Department of Bio-Technology, Birla Institute of Technology Mesra
Retinal Disease Classification from Fundus Images using CNN Transfer Learning
Mar 24, 2026Ali AkramRetinal Disease Classification Medical Image Classification
CliftonLarsonAllen (CLA)
Diabetic Retinopathy Grading with CLIP-based Ranking-Aware Adaptation:A Comparative Study on Fundus Image
Mar 12, 2026Sungjun ChoDiabetic Retinopathy Grading Retinal Disease Classification
Hong Kong University of Science and Technology (HKUST), Hong Kong, China
RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework
Feb 22, 2026Mohammad Tahmid Noor, Shayan Abrar, Jannatul Adan Mahi +3Retinal Disease Classification Medical Image Classification
Dept. of CSE East West University Dhaka, Bangladesh · Dept. of CSE American International University-Bangladesh Dhaka, Bangladesh · Dept. of ICT Bangladesh Army University of Engineering and Technology Dhaka, Bangladesh
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Feb 23, 2025Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8Medical Imaging Diabetic Retinopathy Grading
Munich Center for Machine Learning (MCML), Munich, Germany · School of Medicine and Health, TUM University Hospital, Munich, Germany · School of Computation, Information and Technology, TUM, Munich, Germany
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
May 12, 2024Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath +2Explainable Medical Image Analysis Retinal Vessel Segmentation
aDepartment of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

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Beyond Multi-Stage Pipelines: A Label-Correlation Ensemble for Multi-Label Retinal Fundus Disease Classification

Md Asif Uddin, Jannatul Ferdous, Asif Islam, Zobaer Mahmud Zisan+4

Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh

Multi-Label Classification Retinal Disease Classification Fundus Imaging

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Retinal fundus images frequently carry more than one disease, and public fundus datasets are heterogeneous and severely long-tailed. Clinical triage suggests a staged design, in which a screening gate, a disease-group router and group-specific classifiers operate in sequence, but it is unclear whether this hierarchy earns its complexity. We present a controlled comparison of three architectures on a unified 39-target label space built from RFMiD 1.0, RFMiD 2.0 and ODIR-5K (12,652 images): (i) a three-stage gated cascade of eight models, (ii) a flat model with 39 independent sigmoid outputs, and (iii) a label-correlation model that replaces the flat linear head with 39 learned label queries and a two-layer transformer decoder. All three share one Swin-V2-B backbone, one preprocessing pipeline and one training protocol, so differences are attributable to the architecture. Staging offers no consistent advantage: tracing every missed disease instance to the stage that lost it shows that the two coarse gates discard 34.2% of true instances while the fine-grained classifiers lose only 6.7%, and the gates' hard zeros depress the cascade's macro-AUC to 0.747. Label-correlation modeling gives the largest single gain, raising the detection rate from 0.621 to 0.682 (0.689 with test-time augmentation). Blending the flat and label-correlation models under four-view test-time augmentation is the best configuration on six of eight metrics (exact match 0.694, micro-F1 0.678, macro-F1 0.456, macro-AUC 0.972). On definition-matched class subsets it matches or exceeds four recent studies on macro- and micro-F1, most clearly on low-support pathologies. We also show that ensembling a gated cascade with continuous-output models is harmful, and that per-source negative sampling is needed to remove a dataset shortcut that otherwise yields a spurious AUC of 1.000.