ORCID Profile
0000-0003-3407-9215
Current Organisations
Queen's University
,
Centro de Estudos e Pesquisas do Hospital Sírio Libanês
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Publisher: arXiv
Date: 2022
Publisher: Wiley
Date: 26-04-2017
Publisher: Elsevier BV
Date: 02-2021
Publisher: Frontiers Media SA
Date: 10-2020
Publisher: Springer Science and Business Media LLC
Date: 05-12-2022
DOI: 10.1038/S41467-022-33407-5
Abstract: Although machine learning (ML) has shown promise across disciplines, out-of-s le 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 automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature ( n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large erse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer Science and Business Media LLC
Date: 26-01-2023
Location: Brazil
No related grants have been discovered for Fabio Ynoe Moraes.