Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dou:2021:10.1038/s41746-021-00431-6,
author = {Dou, Q and So, TY and Jiang, M and Liu, Q and Vardhanabhuti, V and Kaissis, G and Li, Z and Si, W and Lee, HHC and Yu, K and Feng, Z and Dong, L and Burian, E and Jungmann, F and Braren, R and Makowski, M and Kainz, B and Rueckert, D and Glocker, B and Yu, SCH and Heng, PA},
doi = {10.1038/s41746-021-00431-6},
journal = {npj Digital Medicine},
pages = {1--11},
title = {Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study},
url = {http://dx.doi.org/10.1038/s41746-021-00431-6},
volume = {4},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.
AU  - Dou,Q
AU  - So,TY
AU  - Jiang,M
AU  - Liu,Q
AU  - Vardhanabhuti,V
AU  - Kaissis,G
AU  - Li,Z
AU  - Si,W
AU  - Lee,HHC
AU  - Yu,K
AU  - Feng,Z
AU  - Dong,L
AU  - Burian,E
AU  - Jungmann,F
AU  - Braren,R
AU  - Makowski,M
AU  - Kainz,B
AU  - Rueckert,D
AU  - Glocker,B
AU  - Yu,SCH
AU  - Heng,PA
DO  - 10.1038/s41746-021-00431-6
EP  - 11
PY  - 2021///
SN  - 2398-6352
SP  - 1
TI  - Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
T2  - npj Digital Medicine
UR  - http://dx.doi.org/10.1038/s41746-021-00431-6
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000634819200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.nature.com/articles/s41746-021-00431-6
UR  - http://hdl.handle.net/10044/1/91815
VL  - 4
ER  -
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