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Title:

Federated learning enables big data for rare cancer boundary detection.

Document type:
Journal Article; Research Support, N.I.H., Extramural
Author(s):
Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pand...     »
Abstract:
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an auto...     »
Journal title abbreviation:
Nat Commun
Year:
2022
Journal volume:
13
Journal issue:
1
Fulltext / DOI:
doi:10.1038/s41467-022-33407-5
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36470898
Print-ISSN:
2041-1723
TUM Institution:
Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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