Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Abstract Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interp...
Saved in:
Main Authors: | Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis, Zeju Li, Weixin Si, Heather H. C. Lee, Kevin Yu, Zuxin Feng, Li Dong, Egon Burian, Friederike Jungmann, Rickmer Braren, Marcus Makowski, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Simon C. H. Yu, Pheng Ann Heng |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/340f463aed8748e1b87b2603e67e7d80 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Medical imaging deep learning with differential privacy
by: Alexander Ziller, et al.
Published: (2021) -
Efficient, high-performance semantic segmentation using multi-scale feature extraction.
by: Moritz Knolle, et al.
Published: (2021) -
Chest-Related Imaging Investigations During Multiple Waves of COVID-19 Infection in Hong Kong
by: Kei Shing Ng, et al.
Published: (2021) -
Location privacy protection scheme for LBS users based ondifferential privacy
by: Naiwen YU, et al.
Published: (2021) -
Multinational monitor
Published: (1980)