Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

OBJECTIVE: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. “Personalized” FL variations have been developed to counter data heterogeneity, but few have been evalua...

Descripción completa

Detalles Bibliográficos
Autores principales: Peng, Le, Luo, Gaoxiang, Walker, Andrew, Zaiman, Zachary, Jones, Emma K, Gupta, Hemant, Kersten, Kristopher, Burns, John L, Harle, Christopher A, Magoc, Tanja, Shickel, Benjamin, Steenburg, Scott D, Loftus, Tyler, Melton, Genevieve B, Gichoya, Judy Wawira, Sun, Ju, Tignanelli, Christopher J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619688/
https://www.ncbi.nlm.nih.gov/pubmed/36214629
http://dx.doi.org/10.1093/jamia/ocac188

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619688/
https://www.ncbi.nlm.nih.gov/pubmed/36214629
http://dx.doi.org/10.1093/jamia/ocac188

Ejemplares similares