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For: 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. Lancet Digit Health 2021;3:e654-64. [PMID: 34417147 DOI: 10.1016/S2589-7500(21)00133-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
2 Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N, Seibel T, Gray R, Hutchins GGA, Brenner H, van Treeck M, Yuan T, Brinker TJ, Chang-Claude J, Khader F, Schuppert A, Luedde T, Trautwein C, Muti HS, Foersch S, Hoffmeister M, Truhn D, Kather JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat Med 2022. [PMID: 35469069 DOI: 10.1038/s41591-022-01768-5] [Reference Citation Analysis]
3 Echle A, Ghaffari Laleh N, Quirke P, Grabsch HI, Muti HS, Saldanha OL, Brockmoeller SF, van den Brandt PA, Hutchins GGA, Richman SD, Horisberger K, Galata C, Ebert MP, Eckardt M, Boutros M, Horst D, Reissfelder C, Alwers E, Brinker TJ, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Chang-Claude J, Brenner H, Trautwein C, Boor P, Jaeger D, Gaisa NT, Hoffmeister M, West NP, Kather JN. Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application. ESMO Open 2022;7:100400. [PMID: 35247870 DOI: 10.1016/j.esmoop.2022.100400] [Reference Citation Analysis]
4 Loeffler CML, Gaisa NT, Muti HS, van Treeck M, Echle A, Ghaffari Laleh N, Trautwein C, Heij LR, Grabsch HI, Ortiz Bruechle N, Kather JN. Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types. Front Genet 2022;12:806386. [DOI: 10.3389/fgene.2021.806386] [Reference Citation Analysis]
5 Cao R, Gong L, Dong D. Pathological diagnosis and prognosis of Gastric cancer through a multi-instance learning method. EBioMedicine 2021;73:103671. [PMID: 34740103 DOI: 10.1016/j.ebiom.2021.103671] [Reference Citation Analysis]
6 Schirris Y, Gavves E, Nederlof I, Horlings HM, Teuwen J. DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102464] [Reference Citation Analysis]
7 Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers (Basel) 2021;13:4919. [PMID: 34638408 DOI: 10.3390/cancers13194919] [Reference Citation Analysis]
8 Hinata M, Ushiku T. Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning. Sci Rep 2021;11:22636. [PMID: 34811485 DOI: 10.1038/s41598-021-02168-4] [Reference Citation Analysis]