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vol. 8 no. 2
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History
- Received October 8, 2021
- Accepted March 4, 2022
- Published online May 3, 2022.
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- previous version (March 24, 2022 - 10:02).
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Copyright ©The authors 2022 This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org
Author Information
- Akshayaa Vaidyanathan1,2,7⇑,
- Julien Guiot3,7,
- Fadila Zerka1,2,
- Flore Belmans1,
- Ingrid Van Peufflik1,
- Louis Deprez4,
- Denis Danthine4,
- Gregory Canivet5,
- Philippe Lambin2,
- Sean Walsh1,
- Mariaelena Occhipinti1,
- Paul Meunier4,
- Wim Vos1,
- Pierre Lovinfosse6 and
- Ralph T.H. Leijenaar1
- 1Radiomics (Oncoradiomics SA), Liège, Belgium
- 2The D-Lab, Depts of Precision Medicine, and Radiology and Nuclear Medicine, GROW–School for Oncology, Maastricht University, Maastricht, The Netherlands
- 3Dept of Pneumology, University Hospital of Liège, Liège, Belgium
- 4Dept of Radiology, University Hospital of Liège, Liège, Belgium
- 5Dept of Computer Applications, University Hospital of Liège, Liège, Belgium
- 6Dept of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
- 7These authors have contributed equally to this work and share first authorship
- Corresponding author: Akshayaa Vaidyanathan (akshayaa.vaidyanathan{at}radiomics.bio)
Funding
European Marie Curie grant PREDICT
766276Horizon 2020 Framework Programme
DRAGON - 101005122; iCOVID – 101016131
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Vol 8 Issue 2
Table of Contents
An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography
