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%0 Journal Article
%A Muti, Hannah Sophie
%A Heij, Lara Rosaline
%A Keller, Gisela
%A Kohlruss, Meike
%A Langer, Rupert
%A Dislich, Bastian
%A Cheong, Jae-Ho
%A Kim, Young-Woo
%A Kim, Hyunki
%A Kook, Myeong-Cherl
%A Cunningham, David
%A Allum, William H.
%A Langley, Ruth E.
%A Nankivell, Matthew G.
%A Quirke, Philip
%A Hayden, Jeremy D.
%A West, Nicholas P.
%A Irvine, Andrew J.
%A Yoshikawa, Takaki
%A Oshima, Takashi
%A Huss, Ralf
%A Grosser, Bianca
%A Roviello, Franco
%A d'Ignazio, Alessia
%A Quaas, Alexander
%A Alakus, Hakan
%A Tan, Xiuxiang
%A Pearson, Alexander T.
%A Luedde, Tom
%A Ebert, Matthias P.
%A Jäger, Dirk
%A Trautwein, Christian
%A Gaisa, Nadine Therese
%A Grabsch, Heike I.
%A Kather, Jakob Nikolas
%T Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer : a retrospective multicentre cohort study
%J The lancet / Digital health
%V 3
%N 10
%@ 2589-7500
%C London
%I The Lancet
%M RWTH-CONV-247332
%P e654-e664
%D 2021
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000704394700010
%$ pmid:34417147
%R 10.1016/S2589-7500(21)00133-3
%U https://publications.rwth-aachen.de/record/845917