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Meta-learning reduces the amount of data needed to build AI models in oncology


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.

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Fig. 1: Meta-learning overview.


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Thanks to Dr. Yeping Lina Qiu and Dr. Pritam Mukherjee for suggestions and edits to this commentary.

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O.G. performed literature review and wrote the manuscript.

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Correspondence to Olivier Gevaert.

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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.

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Gevaert, O. Meta-learning reduces the amount of data needed to build AI models in oncology. Br J Cancer 125, 309–310 (2021).

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