The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A dataset of clinically generated visual questions and answers about radiology images
Open accessAbstract
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.
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Zhang, K., Zhou, R., Adhikarla, E., Yan, Z., Liu, Y., Yu, J., Liu, Z., Chen, X., Davison, B. D., Ren, H., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03185-2 · Cited by 333
Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering
Liu, B., Zhan, L.-M., Xu, L., Ma, L., Yang, Y., & Wu, X.-M. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434010 · Cited by 308
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125
PathVQA: 30000+ Questions for Medical Visual Question Answering
He, X., Zhang, Y., Mou, L., Xing, E., & Xie, P. (2020) · arXiv · doi:10.48550/arxiv.2003.10286 · Cited by 79
Li, Y., Wang, H., & Luo, Y. (2020) · 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313289 · Cited by 81
Metadata
- Authors
- Lau, J. J., Gayen, S., Ben Abacha, A., & Demner-Fushman, D.
- Venue
- Scientific Data
- Published
- 2018-11-20
- Volume
- 5
- Issue
- 1
- Pages
- 180251-180251
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2901466771
- Cited by
- 483
- References
- 9
- Type
- data paper
- Language
- en
- Open access
- gold
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