Descriptor : A dataset of clinically generated visual questions and answers about radiology images
Descriptor : A dataset of clinically generated visual questions and answers about radiology images
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发表时间：
2018
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通讯作者：
J. J. Lau-J.;Soumya Gayen;Asma Ben Abacha;Dina Demner-Fushman
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作者：
J. J. Lau-J.;Soumya Gayen;Asma Ben Abacha;Dina Demner-Fushman
放射学图像是临床决策和人群筛查的重要组成部分，例如，治疗癌症自动化系统可以通过回答有关图像内容的问题来帮助临床医生科普大量图像。作为人工智能的一个新兴领域，医疗领域的视觉问题分类（VQA）探索了这种形式的临床决策支持的方法。这种机器学习工具的成功取决于由针对图像内容的问答对增强的医学图像组成的集合的可用性和设计。我们介绍了VQA-RAD，第一个手动构建的数据集，临床医生询问有关放射学图像的自然发生的问题，并提供参考答案。图像和问题的手动分类提供了对临床相关任务的深入了解以及对它们进行措辞的自然语言。与著名的算法进行评估，我们证明了丰富的质量，这个数据集超过其他自动构建的。我们建议VQARAD鼓励社区设计VQA工具，以改善患者护理为目标。
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers. Manual categorization of images and questions provides insight into clinically relevant tasks and the natural language to phrase them. Evaluating with well-known algorithms, we demonstrate the rich quality of this dataset over other automatically constructed ones. We propose VQARAD to encourage the community to design VQA tools with the goals of improving patient care.