COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
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Title
COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
Authors
Keywords
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Journal
EUROPEAN RADIOLOGY
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-08-02
DOI
10.1007/s00330-020-07087-y
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- (2020) Xiaobo Yang et al. Lancet Respiratory Medicine
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- Ensemble-based classifiers
- (2009) Lior Rokach ARTIFICIAL INTELLIGENCE REVIEW
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