The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering
Open accessAbstract
Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and high-quality labeled datasets for training and evaluation. In this paper, we present a large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA. Besides, SLAKE includes richer modalities and covers more human body parts than the currently available dataset. We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems. The dataset can be downloaded from http://www.med-vqa.com/slake.
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Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590
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Metadata
- Authors
- Liu, B., Zhan, L.-M., Xu, L., Ma, L., Yang, Y., & Wu, X.-M.
- Venue
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2021-04-13
- Pages
- 1650-1654
- OpenAlex
- W3165058054
- Cited by
- 308
- References
- 14
- FWCI
- 9.36
- Type
- conference paper
- Language
- en
- Open access
- green
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- Liu, B., Zhan, L.-M., Xu, L., Ma, L., Yang, Y., & Wu, X.-M. (2021). Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). https://doi.org/10.1109/isbi48211.2021.9434010
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