Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

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dc.contributor.author Solaß, Wiebke
dc.date.accessioned 2020-02-18T12:46:22Z
dc.date.available 2020-02-18T12:46:22Z
dc.date.issued 2019
dc.identifier.issn 1879-0852
dc.identifier.uri http://hdl.handle.net/10900/98156
dc.language.iso en de_DE
dc.publisher Elsevier Sci Ltd de_DE
dc.relation.uri http://dx.doi.org/10.1016/j.ejca.2019.06.012 de_DE
dc.subject.ddc 610 de_DE
dc.title Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images de_DE
dc.type Artikel de_DE
utue.quellen.id 20190926111821_00121
utue.publikation.seiten 91-96 de_DE
utue.personen.roh Hekler, Achim
utue.personen.roh Utikal, Jochen S.
utue.personen.roh Enk, Alexander H.
utue.personen.roh Solass, Wiebke
utue.personen.roh Schmitt, Max
utue.personen.roh Klode, Joachim
utue.personen.roh Schadendorf, Dirk
utue.personen.roh Sondermann, Wiebke
utue.personen.roh Franklin, Cindy
utue.personen.roh Bestvater, Felix
utue.personen.roh Flaig, Michael J.
utue.personen.roh Krahl, Dieter
utue.personen.roh von Kalle, Christof
utue.personen.roh Froehling, Stefan
utue.personen.roh Brinker, Titus J.
dcterms.isPartOf.ZSTitelID European Journal of Cancer de_DE
dcterms.isPartOf.ZS-Volume 118 de_DE
utue.fakultaet 04 Medizinische Fakultät de_DE


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