Can a Novel Deep Neural Network Improve the Computer-Aided Detection of Solid Pulmonary Nodules and the Rate of False-Positive Findings in Comparison to an Established Machine Learning Computer-Aided Detection?

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dc.contributor.author Perl, Regine
dc.contributor.author Hepp, Tobias
dc.contributor.author Horger, Marius
dc.date.accessioned 2021-09-06T11:50:21Z
dc.date.available 2021-09-06T11:50:21Z
dc.date.issued 2021
dc.identifier.issn 1536-0210
dc.identifier.uri http://hdl.handle.net/10900/118624
dc.language.iso en de_DE
dc.publisher Lippincott Williams & Wilkins de_DE
dc.relation.uri http://dx.doi.org/10.1097/RLI.0000000000000713 de_DE
dc.subject.ddc 610 de_DE
dc.title Can a Novel Deep Neural Network Improve the Computer-Aided Detection of Solid Pulmonary Nodules and the Rate of False-Positive Findings in Comparison to an Established Machine Learning Computer-Aided Detection? de_DE
dc.type Article de_DE
utue.quellen.id 20210512015105_01039
utue.publikation.seiten 103-108 de_DE
utue.personen.roh Perl, Regine Mariette
utue.personen.roh Grimmer, Rainer
utue.personen.roh Hepp, Tobias
utue.personen.roh Horger, Marius Stefan
dcterms.isPartOf.ZSTitelID Investigative Radiology de_DE
dcterms.isPartOf.ZS-Issue 2 de_DE
dcterms.isPartOf.ZS-Volume 56 de_DE
utue.fakultaet 04 Medizinische Fakultät de_DE


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