The future of digital health with federated learning
Abstract 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...
Main Authors: | Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2020-09-01
|
Series: | npj Digital Medicine |
Online Access: | https://doi.org/10.1038/s41746-020-00323-1 |
Similar Items
-
Systematic Evaluation of Image Tiling Adverse Effects on Deep Learning Semantic Segmentation
by: G. Anthony Reina, et al.
Published: (2020-02-01) -
ROAM: Random layer mixup for semi‐supervised learning in medical images
by: Tariq Bdair, et al.
Published: (2022-08-01) -
Going to Extremes: Weakly Supervised Medical Image Segmentation
by: Holger R. Roth, et al.
Published: (2021-06-01) -
Transient Features in Charge Fractionalization, Local Equilibration and Non-equilibrium Bosonization
by: Alexander Schneider, Mirco Milletari, Bernd Rosenow
Published: (2017-02-01) -
Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
by: Siddhesh P. Thakur, et al.
Published: (2022-03-01)