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Accounting for data variability in multi-institutional distributed deep learning for medical imaging
OBJECTIVES: Sharing patient data across institutions to train generalizable deep learning models is challenging due to regulatory and technical hurdles. Distributed learning, where model weights are shared instead of patient data, presents an attractive alternative. Cyclical weight transfer (CWT) ha...
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| Gepubliceerd in: | J Am Med Inform Assoc |
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| Hoofdauteurs: | , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Oxford University Press
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7309257/ https://ncbi.nlm.nih.gov/pubmed/32196092 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jamia/ocaa017 |
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