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?

Perl, Regine Mariette; Grimmer, Rainer; Hepp, Tobias; Horger, Marius Stefan

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