Diabetic Retinopathy Grading

Overview

The field studies how to assign ordered severity levels to diabetic retinopathy, usually from color fundus photographs and sometimes from OCT or OCT angiography. Common approaches include convolutional networks, vision transformers and foundation models, with ordinal-learning methods that reflect the progression between grades; lesion detection, anatomical graphs and multimodal fusion aim to make predictions more informative. Models are compared on datasets such as APTOS, EyePACS, Messidor and DDR, using measures such as quadratic weighted kappa, sensitivity and performance across datasets. A central challenge is that strong results on one dataset may not transfer to different cameras or populations, while sparse lesion labels and uncertain predictions complicate safe screening and referral.

Latest papers

CardsList

32 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

  1. Knowledge distillation across teacher architectures for compact diabetic retinopathy grading

    Oct 25, 2026Mossa Manisa, Habiba Jahan Priya, A. K. M. Fakhrul HossainKnowledge DistillationCross-Architecture Knowledge Distillation

  2. 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 GradingRetinal Disease Classification

  3. Explainable Diabetic Retinopathy Classification Using Vision Foundation Models

    Aug 28, 2026Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar +1Retinal Disease ClassificationMedical Image Classification

  4. ORViT-DR: Ordinally-Robust Hybrid ViT for Low-Resolution Diabetic Retinopathy Grading

    Aug 16, 2026Soumit Kumar Kundu, Nabil Ashab, Bidhan Biswas +4Diabetic Retinopathy GradingHybrid CNN-Transformer Architectures

  5. Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading

    Aug 15, 2026Saksham KumarDiabetic Retinopathy GradingRetinal Disease Classification

  6. PARAGraph: Pathology-Anatomy-Aware Hierarchical Graph for Diabetic Retinopathy Grading

    Aug 8, 2026Ziyang Zhang, Yuankai Huo, Yalin Zheng +1Graph Neural NetworksDiabetic Retinopathy Grading

  7. Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging

    Aug 1, 2026Mingya Alexa Gong, Da Ma, Lovre Antonio Budimir +7Medical Imaging Foundation ModelsDiabetic Retinopathy Grading

  8. PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy

    Jul 22, 2026Zübeyr Özeren, Tansel UyarMedical Image Object DetectionDiabetic Retinopathy Grading

  9. Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy

    Jul 6, 2026Inam Ullah, Imran Razzak, Shoaib JameelCausal Mediation AnalysisDiabetic Retinopathy Grading

  10. Model Confidence-Guided Multi-Image Fusion of Fundus Images for Diabetic Retinopathy Diagnosis

    Jul 3, 2026Ananya Raghu, Anisha Raghu, Alice S. Tang +3Multi-Source Image FusionSelective Prediction

  11. A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading

    Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelGraph Representation LearningDiabetic Retinopathy Grading

  12. Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading

    May 10, 2026Yiqun WangDiabetic Retinopathy GradingMedical VLMs

  13. FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy

    May 8, 2026Debashis De, Mahua Nandy Pal, Dipankar HazraDiabetic Retinopathy GradingQuantum Machine Learning

  14. 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 GradingDeep Ensembles

  15. From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs

    Apr 7, 2026Zhe Yu, Wenpeng Xing, Meng HanHallucination DetectionLLM Hallucination Detection

  16. Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview

    Apr 2, 2026Shramana Dey, Zahir Khan, T. A. PramodKumar +5Medical Image BenchmarksDiabetic Retinopathy Grading

  17. Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema

    Mar 9, 2026Pablo Jimenez-Lizcano, Sergio Romero-Tapiador, Ruben Tolosana +4Diabetic Retinopathy GradingFundus Imaging

  18. Weakly Supervised Patch Annotation for Improved Screening of Diabetic Retinopathy

    Mar 4, 2026Shramana Dey, Abhirup Banerjee, B. Uma Shankar +2Weakly Supervised LearningDiabetic Retinopathy Grading

  19. Ordinal Diffusion Models for Color Fundus Images

    Feb 27, 2026Gustav Schmidt, Philipp Berens, Sarah MüllerDiffusion-Based Image GenerationDiffusion Models

  20. Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading

    Feb 25, 2026Haoran Li, Yuxin Lin, Huan Wang +9Diabetic Retinopathy GradingMulti-View Learning

  21. Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images

    Feb 25, 2026Aadi Joshi, Manav S. Sharma, Vijay Uttam Rathod +3Diabetic Retinopathy GradingMedical Image Classification