mlsanityData to 7 Oct 2026

Overcoming Data Limitation in Medical Visual Question Answering

arXiv 1909.11867 ↗First posted 26 Sep 2019cs.CVcited by 71

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Abstract (arXiv)

Traditional approaches for Visual Question Answering (VQA) require large amount of labeled data for training. Unfortunately, such large scale data is usually not available for medical domain. In this paper, we propose a novel medical VQA framework that overcomes the labeled data limitation. The proposed framework explores the use of the unsupervised Denoising Auto-Encoder (DAE) and the supervised Meta-Learning. The advantage of DAE is to leverage the large amount of unlabeled images while the advantage of Meta-Learning is to learn meta-weights that quickly adapt to VQA problem with limited labeled data. By leveraging the advantages of these techniques, it allows the proposed framework to be efficiently trained using a small labeled training set. The experimental results show that our proposed method significantly outperforms the state-of-the-art medical VQA.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2019.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 20; 2024: 16; 2025: 14; 2026: 4.

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  1. 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 444
  2. 2023PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answeringcited by 333
  3. 2023PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documentscited by 212
  4. 2022Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Trainingcited by 104
  5. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
  6. 2021Medical Visual Question Answering: A Surveycited by 93
  7. 2021Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?cited by 85
  8. 2021MMBERT: Multimodal BERT Pretraining for Improved Medical VQAcited by 68
  9. 2023Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Modelscited by 58
  10. 2021Multiple Meta-model Quantifying for Medical Visual Question Answeringcited by 54
  11. 2022Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledgecited by 51
  12. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
38 more
  1. 2024MedCoT: Medical Chain of Thought via Hierarchical Expertcited by 46
  2. 2025Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysiscited by 44
  3. 2025Improving Medical Large Vision-Language Models with Abnormal-Aware Feedbackcited by 42
  4. 2023Towards Unifying Medical Vision-and-Language Pre-training via Soft Promptscited by 35
  5. 2023RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-trainingcited by 31
  6. 2023Q2ATransformer: Improving Medical VQA via an Answer Querying Decodercited by 31
  7. 2022Self-supervised vision-language pretraining for Medical visual question answeringcited by 30
  8. 2023Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answeringcited by 29
  9. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 28
  10. 20253D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Taskscited by 24
  11. 2023Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-trainingcited by 22
  12. 2024R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interestcited by 21
  13. 2024Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Surveycited by 20
  14. 2023Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reportingcited by 17
  15. 2022RepsNet: Combining Vision with Language for Automated Medical Reportscited by 14
  16. 2024MISS: A Generative Pretraining and Finetuning Approach for Med-VQAcited by 14
  17. 2024MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationalecited by 14
  18. 2025MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbookscited by 14
  19. 2023A scoping review on multimodal deep learning in biomedical images and textscited by 13
  20. 2024LaPA: Latent Prompt Assist Model For Medical Visual Question Answeringcited by 12
  21. 2021Visual Question Answering: which investigated applications?cited by 11
  22. 2023Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretrainingcited by 11
  23. 2024Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Modelscited by 11
  24. 2022Consistency-preserving Visual Question Answering in Medical Imagingcited by 10
  25. 2022UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answeringcited by 6
  26. 2023Logical Implications for Visual Question Answering Consistencycited by 6
  27. 2024Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquirycited by 6
  28. 2025Structure Causal Models and LLMs Integration in Medical Visual Question Answeringcited by 6
  29. 2022A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answeringcited by 5
  30. 2023Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmarkcited by 5
  31. 2023Localized Questions in Medical Visual Question Answeringcited by 5
  32. 2025SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imagingcited by 5
  33. 2021MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answeringcited by 4
  34. 2024Prompt-based Personalized Federated Learning for Medical Visual Question Answeringcited by 4
  35. 2023MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaptioncited by 3
  36. 2023BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answeringcited by 3
  37. 2023UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Promptscited by 3
  38. 2024Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuningcited by 3

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