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Titel:

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.

Dokumenttyp:
Journal Article
Autor(en):
Dou, Qi; So, Tiffany Y; Jiang, Meirui; Liu, Quande; Vardhanabhuti, Varut; Kaissis, Georgios; Li, Zeju; Si, Weixin; Lee, Heather H C; Yu, Kevin; Feng, Zuxin; Dong, Li; Burian, Egon; Jungmann, Friederike; Braren, Rickmer; Makowski, Marcus; Kainz, Bernhard; Rueckert, Daniel; Glocker, Ben; Yu, Simon C H; Heng, Pheng Ann
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 interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalitie...     »
Zeitschriftentitel:
NPJ Digit Med
Jahr:
2021
Band / Volume:
4
Heft / Issue:
1
Volltext / DOI:
doi:10.1038/s41746-021-00431-6
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/33782526
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie; Institut für Medizinische Statistik und Epidemiologie
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