An international study presenting a federated learning AI platform for pediatric brain tumors

Abstract While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Her...

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主要な著者:	Edward H. Lee, Michelle Han, Jason Wright, Michael Kuwabara, Jacob Mevorach, Gang Fu, Olivia Choudhury, Ujjwal Ratan, Michael Zhang, Matthias W. Wagner, Robert Goetti, Sebastian Toescu, Sebastien Perreault, Hakan Dogan, Emre Altinmakas, Maryam Mohammadzadeh, Kathryn A. Szymanski, Cynthia J. Campen, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Kshitij Mankad, Kristian Aquilina, Mourad Said, Arastoo Vossough, Ozgur Oztekin, Birgit Ertl-Wagner, Tina Poussaint, Eric M. Thompson, Chang Y. Ho, Alok Jaju, John Curran, Vijay Ramaswamy, Samuel H. Cheshier, Gerald A. Grant, S. Simon Wong, Michael E. Moseley, Robert M. Lober, Mattias Wilms, Nils D. Forkert, Nicholas A. Vitanza, Jeffrey H. Miller, Laura M. Prolo, Kristen W. Yeom
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2024-09-01
シリーズ:	Nature Communications
オンライン･アクセス:	https://doi.org/10.1038/s41467-024-51172-5
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