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Marmé, Frederik ; Krieghoff-Henning, Eva ; Gerber, Bernd ; Schmitt, Max ; Zahm, Dirk-Michael ; Bauerschlag, Dirk ; Forstbauer, Helmut ; Hildebrandt, Guido ; Ataseven, Beyhan ; Brodkorb, Tobias ; Denkert, Carsten ; Stachs, Angrit ; Krug, David ; Heil, Jörg ; Golatta, Michael ; Kühn, Thorsten ; Nekljudova, Valentina ; Gaiser, Timo ; Schönmehl, Rebecca ; Brochhausen, Christoph ; Loibl, Sibylle ; Reimer, Toralf ; Brinker, Titus J.

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images

Marmé, Frederik, Krieghoff-Henning, Eva, Gerber, Bernd, Schmitt, Max, Zahm, Dirk-Michael, Bauerschlag, Dirk, Forstbauer, Helmut, Hildebrandt, Guido, Ataseven, Beyhan, Brodkorb, Tobias, Denkert, Carsten, Stachs, Angrit, Krug, David, Heil, Jörg, Golatta, Michael, Kühn, Thorsten, Nekljudova, Valentina, Gaiser, Timo, Schönmehl, Rebecca, Brochhausen, Christoph, Loibl, Sibylle, Reimer, Toralf and Brinker, Titus J. (2023) Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images. European Journal of Cancer 195, p. 113390.

Date of publication of this fulltext: 18 Mar 2025 10:10
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Details

Item typeArticle
Journal or Publication TitleEuropean Journal of Cancer
Publisher:ELSEVIER SCI LTD
Place of Publication:OXFORD
Volume:195
Page Range:p. 113390
Date2023
InstitutionsMedicine > Lehrstuhl für Pathologie
Identification Number
ValueType
10.1016/j.ejca.2023.113390DOI
Keywords; Sentinel; Lymph node status; Deep learning; Breast cancer; Digital biomarker
Dewey Decimal Classification600 Technology > 610 Medical sciences Medicine
StatusPublished
RefereedYes, this version has been refereed
Created at the University of RegensburgYes
Item ID76185

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