Cargando…
The future of digital health with federated learning
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and...
Autores principales: | Rieke, Nicola, Hancox, Jonny, Li, Wenqi, Milletarì, Fausto, Roth, Holger R., Albarqouni, Shadi, Bakas, Spyridon, Galtier, Mathieu N., Landman, Bennett A., Maier-Hein, Klaus, Ourselin, Sébastien, Sheller, Micah, Summers, Ronald M., Trask, Andrew, Xu, Daguang, Baust, Maximilian, Cardoso, M. Jorge |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490367/ https://www.ncbi.nlm.nih.gov/pubmed/33015372 http://dx.doi.org/10.1038/s41746-020-00323-1 |
Ejemplares similares
-
OpenFL: the open federated learning library
por: Foley, Patrick, et al.
Publicado: (2022) -
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
por: Sheller, Micah J., et al.
Publicado: (2020) -
Federated learning for medical imaging radiology
por: Rehman, Muhammad Habib ur, et al.
Publicado: (2023) -
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan()
por: Yang, Dong, et al.
Publicado: (2021) -
Federated learning improves site performance in multicenter deep learning without data sharing
por: Sarma, Karthik V, et al.
Publicado: (2021)