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Cited by in F6Publishing
For: Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut. 2020;. [PMID: 33214163 DOI: 10.1136/gutjnl-2020-322880] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
2 Janssen BV, Theijse R, van Roessel S, de Ruiter R, Berkel A, Huiskens J, Busch OR, Wilmink JW, Kazemier G, Valkema P, Farina A, Verheij J, de Boer OJ, Besselink MG. Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment. Cancers (Basel) 2021;13:5089. [PMID: 34680241 DOI: 10.3390/cancers13205089] [Reference Citation Analysis]
3 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
4 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
5 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]
6 Neuberger J, Cain O. The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics. Hepat Med 2021;13:59-69. [PMID: 34163263 DOI: 10.2147/HMER.S278076] [Reference Citation Analysis]
7 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]