mlsanityData to 7 Oct 2026

DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation

arXiv 2011.00569 ↗First posted 1 Nov 2020cs.CV cs.AI cs.CLcited by 43

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Abstract (arXiv)

In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 2; 2023: 7; 2024: 15; 2025: 10; 2026: 3.

43 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2023A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervisioncited by 78
  3. 2021The Dawn of Quantum Natural Language Processingcited by 60
  4. 2021GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarizationcited by 26
  5. 2021Automated Generation of Accurate & Fluent Medical X-ray Reportscited by 24
  6. 2022Cross-modal Clinical Graph Transformer for Ophthalmic Report Generationcited by 24
  7. 2024VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledgecited by 19
  8. 2024Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metricscited by 17
  9. 2021Contextualized Keyword Representations for Multi-modal Retinal Image Captioningcited by 16
  10. 2021Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"cited by 16
  11. 2023Query-based Video Summarization with Pseudo Label Supervisioncited by 13
  12. 2024Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Modelcited by 12
31 more
  1. 2024A Novel Evaluation Framework for Image2Text Generationcited by 11
  2. 2024FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Taskscited by 11
  3. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  4. 2024UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modelingcited by 10
  5. 2023Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questionscited by 9
  6. 2024QFMTS: Generating Query-Focused Summaries over Multi-Table Inputscited by 9
  7. 2025Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learningcited by 9
  8. 2021LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancementcited by 8
  9. 2023Causalainer: Causal Explainer for Automatic Video Summarizationcited by 8
  10. 2025Advancing Medical Representation Learning Through High-Quality Datacited by 8
  11. 2023Conditional Modeling Based Automatic Video Summarizationcited by 6
  12. 2024A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Datacited by 6
  13. 2025Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearningcited by 6
  14. 2024Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Modelscited by 5
  15. 2025MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretrainingcited by 5
  16. 2025Unleashing the Potential of Large Language Models for Text-to-Image Generation through Autoregressive Representation Alignmentcited by 5
  17. 2023Causal Video Summarizer for Video Explorationcited by 4
  18. 2024M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generationcited by 3
  19. 2025A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Modelscited by 3
  20. 2024Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathycited by 2
  21. 2024A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learningcited by 1
  22. 2024Visual Question Answering in Ophthalmology: A Progressive and Practical Perspectivecited by 1
  23. 2024GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioningcited by 1
  24. 2025A Shared Encoder Approach to Multimodal Representation Learningcited by 1
  25. 2025Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barrierscited by 1
  26. 2025HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Taskscited by 1
  27. 2025DeepEyeNet: Generating Medical Report for Retinal Imagescited by 1
  28. 2023Towards Interpretable Classification of Leukocytes based on Deep Learningcited by 0
  29. 2026DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generationcited by 0
  30. 2026Scientific Domain Knowledge Improves Vision-Language Fundus Modelscited by 0
  31. 2026EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Groundingcited by 0