# Pathological Visual Question Answering

arXiv: https://arxiv.org/abs/2010.12435 · HTML: https://mlsanity.com/paper/2010.12435/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 18 of 18)

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- [Multiple Meta-model Quantifying for Medical Visual Question Answering](https://mlsanity.com/paper/2105.08913/) (2021)
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- [MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models](https://mlsanity.com/paper/2402.09262/) (2024)
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- [BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities](https://mlsanity.com/paper/2412.07769/) (2024)
- [Hallucination Benchmark in Medical Visual Question Answering](https://mlsanity.com/paper/2401.05827/) (2024)
- [When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models](https://mlsanity.com/paper/2603.23085/) (2026)
- [Self-supervised Regularization for Text Classification](https://mlsanity.com/paper/2103.05231/) (2021)
- [TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots](https://mlsanity.com/paper/2511.17652/) (2025)
- [BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering](https://mlsanity.com/paper/2312.07867/) (2023)
- [Proactive Reasoning-with-Retrieval Framework for Medical Multimodal Large Language Models](https://mlsanity.com/paper/2510.18303/) (2025)
- [MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites](https://mlsanity.com/paper/2510.12126/) (2025)
- [MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation](https://mlsanity.com/paper/2607.09142/) (2026)

## Abstract (arXiv)

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology (ABP)? To build such a system, three challenges need to be addressed. First, we need to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer. Due to privacy concerns, pathology images are usually not publicly available. Besides, only well-trained pathologists can understand pathology images, but they barely have time to help create datasets for AI research. The second challenge is: since it is difficult to hire highly experienced pathologists to create pathology visual questions and answers, the resulting pathology VQA dataset may contain errors. Training pathology VQA models using these noisy or even erroneous data will lead to problematic models that cannot generalize well on unseen images. The third challenge is: the medical concepts and knowledge covered in pathology question-answer (QA) pairs are very diverse while the number of QA pairs available for modeling training is limited. How to learn effective representations of diverse medical concepts based on limited data is technically demanding. In this paper, we aim to address these three challenges. To our best knowledge, our work represents the first one addressing the pathology VQA problem. To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset. To address the second challenge, we propose a learning-by-ignoring approach. To address the third challenge, we propose to use cross-modal self-supervised learning. We perform experiments on our created PathVQA dataset and the results demonstrate the effectiveness of our proposed learning-by-ignoring method and cross-modal self-supervised learning methods.