Summary
Meta-learning is showing promise in recent genomic studies in oncology. Meta-learning can facilitate transfer learning and reduce the amount of data that is needed in a target domain by transferring knowledge from abundant genomic data in different source domains enabling the use of AI in data scarce scenarios.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 24 print issues and online access
$259.00 per year
only $10.79 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Fei-Fei, L., Fergus, R. & Perona, P. One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28, 594–611 (2006).
Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. Preprint at https://arxiv.org/abs/1902.07208 (2019).
Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. in International Conference on Machine Learning. (eds Precup, D. & Teh, Y. W.) 1126–1135 (PMLR, 2017).
Qiu, Y. L., Zheng, H., Devos, A., Selby, H. & Gevaert, O. A meta-learning approach for genomic survival analysis. Nat. Commun. 11, 6350 (2020).
Finn, C., Abbeel, P. & Levine, S. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. in Proceedings of the 34th International Conference on Machine Learning. Vol. 70 (eds Precup, D. & Teh, Y. W.) 1126–1135 (PMLR, International Convention Centre, Sydney, 2017).
Pérez-Rúa, J.-M., Zhu, X., Hospedales, T. M. & Xiang, T. Incremental few-shot object detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13843–13852 (IEEE, Seattle, WA, 2020).
Liang-Yan, G., Wang, Y.-X., Ramanan, D. & Moura, J. M. Few-shot human motion prediction via meta-learning. in Proceedings of the European Conference on Computer Vision (ECCV). (eds Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 432–450 (Springer, 2018).
Shaban, A., Bansal, S., Liu, Z., Essa, I. & Boots, B. One-shot learning for semantic segmentation. Preprint at https://arxiv.org/abs/1709.03410 (2017).
Brbic, M., Zitnik, M., Wang, S., Pisco, A. O., Altman, R. B., Darmanis, S. et al. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17, 1200–1206 (2020).
Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S. et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat. Cancer 2, 233–244 (2021).
Acknowledgements
Thanks to Dr. Yeping Lina Qiu and Dr. Pritam Mukherjee for suggestions and edits to this commentary.
Author information
Authors and Affiliations
Contributions
O.G. performed literature review and wrote the manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent to publish
Not applicable.
Data availability
Not applicable.
Competing interests
The author declares no competing interests.
Funding information
O.G. is supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R56 EB020527. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gevaert, O. Meta-learning reduces the amount of data needed to build AI models in oncology. Br J Cancer 125, 309–310 (2021). https://doi.org/10.1038/s41416-021-01358-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41416-021-01358-1
