Federated learning improves site performance in multicenter deep learning without data sharing | Zendy

Karthik V. Sarma | Zendy; Stephanie A. Harmon | Zendy; Thomas Sanford | Zendy; Holger R. Roth | Zendy; Ziyue Xu | Zendy; Jesse Tetreault | Zendy; Daguang Xu | Zendy; Mona G. Flores | Zendy; A. Raman | Zendy; Rushikesh Kulkarni | Zendy; Bradford J. Wood | Zendy; Peter L. Choyke | Zendy; Alan Priester | Zendy; Leonard S. Marks | Zendy; Steven S. Raman | Zendy; Dieter R. Enzmann | Zendy; Barış Türkbey | Zendy; William Speier | Zendy; Corey Arnold | Zendy

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Federated learning improves site performance in multicenter deep learning without data sharing

Author(s) -

Karthik V. Sarma,

Stephanie A. Harmon,

Thomas Sanford,

Holger R. Roth,

Ziyue Xu,

Jesse Tetreault,

Daguang Xu,

Mona G. Flores,

A. Raman,

Rushikesh Kulkarni,

Bradford J. Wood,

Peter L. Choyke,

Alan Priester,

Leonard S. Marks,

Steven S. Raman,

Dieter R. Enzmann,

Barış Türkbey,

William Speier,

Corey Arnold

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa341

Subject(s) - generalizability theory , pooling , computer science , artificial intelligence , federated learning , transfer of learning , institution , deep learning , data sharing , machine learning , training set , psychology , medicine , political science , developmental psychology , alternative medicine , pathology , law

To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

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