PathVQA: 30000+ Questions for Medical Visual Question Answering
PathVQA: 30000+ Questions for Medical Visual Question Answering
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发表时间：
2020-03
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通讯作者：
Xuehai He;Yichen Zhang;Luntian Mou;E. Xing;P. Xie
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作者：
Xuehai He;Yichen Zhang;Luntian Mou;E. Xing;P. Xie
有没有可能培养出“AI病理学家”，通过美国病理学委员会的委员会认证考试？为了实现这一目标，第一步是创建一个视觉问答（VQA）数据集，其中AI代理将与病理图像以及问题一起呈现，并被要求给出正确的答案。我们的工作首次尝试建立这样的数据集。与创建通用域VQA数据集不同，在通用域VQA数据集中，图像可以广泛访问，并且有许多众包工作人员可以生成问答对，开发医疗VQA数据集更具挑战性。首先，由于隐私问题，病理图像通常不公开。其次，只有训练有素的病理学家才能理解病理图像，但他们几乎没有时间帮助创建人工智能研究的数据集。为了应对这些挑战，我们求助于病理学教科书和在线数字图书馆。我们开发了一个半自动化的管道，从教科书中提取病理图像和标题，并使用自然语言处理从标题中生成问答对。我们从4,998张病理图像中收集了32,799个开放式问题，每个问题都经过手动检查以确保正确性。据我们所知，这是病理学VQA的第一个数据集。我们的数据集将公开发布，以促进医学VQA的研究。
Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, the first step is 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. Our work makes the first attempt to build such a dataset. Different from creating general-domain VQA datasets where the images are widely accessible and there are many crowdsourcing workers available and capable of generating question-answer pairs, developing a medical VQA dataset is much more challenging. First, due to privacy concerns, pathology images are usually not publicly available. Second, only well-trained pathologists can understand pathology images, but they barely have time to help create datasets for AI research. To address these challenges, we resort to pathology textbooks and online digital libraries. We develop a semi-automated pipeline to extract pathology images and captions from textbooks and generate question-answer pairs from captions using natural language processing. We collect 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness. To our best knowledge, this is the first dataset for pathology VQA. Our dataset will be released publicly to promote research in medical VQA.