To address the challenge of pretraining foundational models with large datasets, a multi-task approach is proposed, thus helping to overcome the data scarcity problem in biomedical imaging.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Achiam, J. et al. Preprint at https://arxiv.org/abs/2303.08774 (2023).
Bubeck, S. et al. Preprint at https://arxiv.org/abs/2303.12712 (2023).
Schäfer, R. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00662-z (2024).
Litjens, G. et al. Med. Image Anal. 42, 60–88 (2017).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Nat. Med. 28, 31–38 (2022).
Glocker, B., Robinson, R., Castro, D. C., Dou, Q. & Konukoglu, E. Preprint at https://arxiv.org/abs/1910.04597 (2019).
Moor, M. et al. Nature 616, 259–265 (2023).
Yang, J. et al. Sci. Data 10, 41 (2023).
Zhou, H.-Y. et al. Nat. Mach. Intell. 4, 32–40 (2022).
Li, W., Yuille, A. & Zhou, Z. How well do supervised models transfer to 3D image segmentation? In International Conference on Learning Representations (ICLR, 2024).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
J.Y. holds equity in Dianei Technology but believes this does not constitute a competing interest.
Rights and permissions
About this article
Cite this article
Yang, J. Multi-task learning for medical foundation models. Nat Comput Sci 4, 473–474 (2024). https://doi.org/10.1038/s43588-024-00658-9
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-024-00658-9
