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Cited by in F6Publishing
For: Pai RK, Hartman D, Schaeffer DF, Rosty C, Shivji S, Kirsch R, Pai RK. Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters. Histopathology 2021;79:391-405. [PMID: 33590485 DOI: 10.1111/his.14353] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, von Kalle C, Fröhling S, Alwers E, Brenner H, Hoffmeister M, Brinker TJ. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer 2021;157:464-73. [PMID: 34649117 DOI: 10.1016/j.ejca.2021.08.039] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Fisher NC, Loughrey MB, Coleman HG, Gelbard MD, Bankhead P, Dunne PD. Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods. Histopathology 2021. [PMID: 34580909 DOI: 10.1111/his.14574] [Reference Citation Analysis]