The future of digital health with federated learning
The future of digital health with federated learning
About this item
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Author / Creator
Rieke, Nicola , Hancox, Jonny , Li, Wenqi , Milletarì, Fausto , Roth, Holger R. , Albarqouni, Shadi , Bakas, Spyridon , Galtier, Mathieu N. , Landman, Bennett A. , Maier-Hein, Klaus , Ourselin, Sébastien , Sheller, Micah , Summers, Ronald M. , Trask, Andrew , Xu, Daguang , Baust, Maximilian and Cardoso, M. Jorge
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. Ho...
Alternative Titles
Full title
The future of digital health with federated learning
Authors, Artists and Contributors
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_32cc7c58a8074ffe8d62799e5fe58178
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_32cc7c58a8074ffe8d62799e5fe58178
Other Identifiers
ISSN
2398-6352
E-ISSN
2398-6352
DOI
10.1038/s41746-020-00323-1
