Work

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

Year: 2021

Type: article

Source: British Journal of Cancer

DOI: 10.1038/s41416-021-01358-1

Author: Olivier Gevaert

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Health and Human Services, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering

Awards: Radiogenomics Framework for Non-Invasive Personalized Medicine, G4593691785, Radiogenomics framework for non-invasive personalized medicine, Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation

OpenAlex ID: W3145099584

FWCI: 0.762

Cites: 11

Cited by: 13

Related to: 10