# Overcoming Data Limitation in Medical Visual Question Answering

arXiv: https://arxiv.org/abs/1909.11867 · HTML: https://mlsanity.com/paper/1909.11867/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 30 of 71)

- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training](https://mlsanity.com/paper/2209.07098/) (2022)
- [Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training](https://mlsanity.com/paper/2105.11333/) (2021)
- [Medical Visual Question Answering: A Survey](https://mlsanity.com/paper/2111.10056/) (2021)
- [Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?](https://mlsanity.com/paper/2112.13906/) (2021)
- [MMBERT: Multimodal BERT Pretraining for Improved Medical VQA](https://mlsanity.com/paper/2104.01394/) (2021)
- [Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models](https://mlsanity.com/paper/2303.05977/) (2023)
- [Multiple Meta-model Quantifying for Medical Visual Question Answering](https://mlsanity.com/paper/2105.08913/) (2021)
- [Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge](https://mlsanity.com/paper/2209.07118/) (2022)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [MedCoT: Medical Chain of Thought via Hierarchical Expert](https://mlsanity.com/paper/2412.13736/) (2024)
- [Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis](https://mlsanity.com/paper/2503.20047/) (2025)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts](https://mlsanity.com/paper/2302.08958/) (2023)
- [RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training](https://mlsanity.com/paper/2303.00534/) (2023)
- [Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder](https://mlsanity.com/paper/2304.01611/) (2023)
- [Self-supervised vision-language pretraining for Medical visual question answering](https://mlsanity.com/paper/2211.13594/) (2022)
- [Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering](https://mlsanity.com/paper/2307.05314/) (2023)
- [MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models](https://mlsanity.com/paper/2402.09262/) (2024)
- [3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks](https://mlsanity.com/paper/2506.11147/) (2025)
- [Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training](https://mlsanity.com/paper/2305.07920/) (2023)
- [R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest](https://mlsanity.com/paper/2410.20327/) (2024)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting](https://mlsanity.com/paper/2307.05766/) (2023)
- [RepsNet: Combining Vision with Language for Automated Medical Reports](https://mlsanity.com/paper/2209.13171/) (2022)
- [MISS: A Generative Pretraining and Finetuning Approach for Med-VQA](https://mlsanity.com/paper/2401.05163/) (2024)
- [MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale](https://mlsanity.com/paper/2404.12372/) (2024)
- [MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks](https://mlsanity.com/paper/2505.06152/) (2025)

## 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.