Journal Article

DKFZ-2019-01487

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.

Kather, J. N. (First author)DKFZ* ; Pearson, A. T. ; Halama, N.DKFZ* ; Jäger, D.DKFZ* ; Krause, J. ; Loosen, S. H. ; Marx, A. ; Boor, P. ; Tacke, F. ; Neumann, U. P. ; Grabsch, H. I. ; Yoshikawa, T. ; Brenner, H.DKFZ* ; Chang-Claude, J.DKFZ* ; Hoffmeister, M.DKFZ* ; Trautwein, C. ; Luedde, T.

2019
Nature America Inc. New York, NY

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Please use a persistent id in citations: doi:10.1038/s41591-019-0462-y

Abstract: Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.

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Contributing Institute(s):

1. Angewandte Tumor-Immunität (D120)
2. Translationale Immuntherapie (D240)
3. Klinische Epidemiologie und Alternsforschung (C070)
4. Präventive Onkologie (C120)
5. Epidemiologie von Krebserkrankungen (C020)
6. DKTK Heidelberg (L101)

Research Program(s):

1. 314 - Tumor immunology (POF3-314) (POF3-314)

Appears in the scientific report 2019

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Database coverage:
; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 30 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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