Publications on deep learning

This is a list of publications on deep learning by members of our team:

Muti HS, Heij LR, Keller G, Kohlruss M, Langer R, Dislich B, Cheong JH, Kim YW, Kim H, Kook MC, Cunningham D, Allum WH, Langley RE, Nankivell MG, Quirke P, Hayden JD, West NP, Irvine AJ, Yoshikawa T, Oshima T, Huss R, Grosser B, Roviello F, d’Ignazio A, Quaas A, Alakus H, Tan X, Pearson AT, Luedde T, Ebert MP, Jäger D, Trautwein C, Gaisa NT, Grabsch HI, Kather JN. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. The Lancet Digital Health, 2021, doi: 10.1016/S2589-7500(21)00133-3.

Krause J, Grabsch HI, Kloor M, Jendrusch M, Echle A, Buelow RD, Boor P, Luedde T, Brinker TJ, Trautwein C, Pearson AT, Quirke P, Jenniskens J, Offermans K, van den Brandt PA, Kather JN. Deep learning detects genetic alterations in cancer histology generated by adversarial networks. The Journal of Pathology, 2021, doi: 10.1002/path.5638

Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut, 2020 doi: 10.1136/gutjnl-2020-322880. Epub ahead of print.

Kather JN, Heij LR , Grabsch HI , Loeffler C , Echle A , Muti HS, Krause J, Niehues JM, Sommer KA, Bankhead P, Kooreman LFS, Schulte J, Cipriani NA , Bülow RD, Boor P, Ortiz Bruechle N, Hanby AM, Speirs V, Kochanny, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jaeger D, Trautwein C, Pearson AT, Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer, 2020, doi: 10.1038/s43018-020-0087-6

Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GGA, Heij LR, Tan X, Richman SD, Krause J, Alwers E, Jenniskens J, Offermans K, Gray R, Brenner H, Chang-Claude J, Trautwein C, Pearson AT, Boor P, Luedde T, Gaisa NT, Hoffmeister M, Kather JN. Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. Gastroenterology, 2020, doi: 10.1053/j.gastro.2020.06.021

Kather JN, Pearson AT, Halama N, Jaeger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature Medicine, 2019, doi: 10.1038/s41591-019-0462-y

Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, Gaiser T, Marx A, Valous NA, Ferber D, Jansen L, Reyes-Aldasoro CC, Zörnig I, Jäger D, Brenner H, Chang-Clause J, Hoffmeister M, Halama N. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLOS Medicine, 2019, doi: 10.1371/journal.pmed.1002730

Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zöllner FG. Multi-class texture analysis in colorectal cancer histology. Scientific Reports, 2016, doi: 10.1038/srep27988