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Self-supervised learning in medicine and healthcare

Published:2022-08-11 Issue:12 Volume:6 Page:1346-1352
ISSN:2157-846X
Container-title:Nature Biomedical Engineering
language:en
Short-container-title:Nat. Biomed. Eng

Author:

Krishnan Rayan^ORCID,Rajpurkar Pranav^ORCID,Topol Eric J.^ORCID

Funder

U.S. Department of Health & Human Services | National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology

Link

https://www.nature.com/articles/s41551-022-00914-1.pdf

Reference60 articles.

1. Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).

2. Sambasivan, N. et al. “Everyone wants to do the model work, not the data work”: data cascades in high-stakes AI. In Proc. 2021 CHI Conference on Human Factors in Computing Systems (Association for Computing Machinery, 2021); https://doi.org/10.1145/3411764.3445518

3. Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).

4. Irvin, J. et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 590–597 (AAAI Press, 2019).

5. Huh, M., Agrawal, P. & Efros, A. What makes ImageNet good for transfer learning? Preprint at https://doi.org/10.48550/arXiv.1608.08614 (2016).

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