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Medical Visual Question Answering: A Survey

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arxiv 2111.10056 v3 pith:LLTCWY4C submitted 2021-11-19 cs.CV cs.AI

Medical Visual Question Answering: A Survey

classification cs.CV cs.AI
keywords medicalquestionansweringdiscussfieldpartvisualchallenges
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.

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Cited by 1 Pith paper

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  1. PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

    cs.CV 2023-05 conditional novelty 6.0

    PMC-VQA dataset and MedVInT model achieve better generative performance on medical VQA benchmarks by visual instruction tuning on a newly constructed large-scale dataset.