Year: 2018
Type: data-paper
Abstract: 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 VQA-RAD to encourage the community to design VQA tools with the goals of improving patient care.
Source: Scientific Data
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: Foundation for the National Institutes of Health, Doris Duke Charitable Foundation, Colgate-Palmolive Company, American Association for Dental, Oral, and Craniofacial Research, National Institutes of Health, Genentech, Uniformed Services University of the Health Sciences, U.S. National Library of Medicine
OpenAlex ID: W2901466771