Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training

dc.contributor.author

Fricks, Rafael B

dc.contributor.author

Ria, Francesco

dc.contributor.author

Chalian, Hamid

dc.contributor.author

Khoshpouri, Pegah

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Abadi, Ehsan

dc.contributor.author

Bianchi, Lorenzo

dc.contributor.author

Segars, William P

dc.contributor.author

Samei, Ehsan

dc.date.accessioned

2021-12-03T16:17:50Z

dc.date.available

2021-12-03T16:17:50Z

dc.date.issued

2021-12-01

dc.date.updated

2021-12-03T16:17:49Z

dc.identifier.issn

2329-4302

dc.identifier.uri

https://hdl.handle.net/10161/24036

dc.publisher

SPIE-Intl Soc Optical Eng

dc.relation.ispartof

Journal of Medical Imaging

dc.relation.isversionof

10.1117/1.jmi.8.6.064501

dc.title

Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training

dc.type

Journal article

duke.contributor.orcid

Ria, Francesco|0000-0001-5902-7396

duke.contributor.orcid

Abadi, Ehsan|0000-0002-9123-5854

duke.contributor.orcid

Samei, Ehsan|0000-0001-7451-3309

pubs.issue

06

pubs.organisational-group

Staff

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Radiology

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Duke

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Clinical Science Departments

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School of Medicine

pubs.publication-status

Published

pubs.volume

8

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