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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Year of publication

2021

Authors

AIX-COVNET; Roberts, Michael; Driggs, Derek; Thorpe, Matthew; Gilbey, Julian; Yeung, Michael; Ursprung, Stephan; Aviles-Rivero, Angelica I.; Etmann, Christian; McCague, Cathal; Beer, Lucian; Weir-McCall, Jonathan R.; Teng, Zhongzhao; Gkrania-Klotsas, Effrossyni; Ruggiero, Alessandro; Korhonen, Anna; Jefferson, Emily; Ako, Emmanuel; Langs, Georg; Gozaliasl, Ghassem
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Organizations and authors

University of Helsinki

Gozaliasl Ghassem

Tang Jing

Shadbahr Tolou

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Journal

Nature Machine Intelligence

Parent publication name

Nature Machine Intelligence

Volume

3

Issue

3

Pages

199-217

ISSN

2522-5839

​Publication forum

88443

​Publication forum level

1

Open access

Open access in the publisher’s service

Yes

Open access of publication channel

Partially open publication channel

License of the publisher’s version

CC BY

Self-archived

Yes

License of the self-archived publication

CC BY

Other information

Fields of science

Mathematics; Computer and information sciences; Cancers

Keywords

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Publication country

United Kingdom

Internationality of the publisher

International

Language

English

International co-publication

Yes

Co-publication with a company

Yes

DOI

10.1038/s42256-021-00307-0

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes