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Cited by in F6Publishing
For: Hwang M, Wang D, Kong XX, Wang Z, Li J, Jiang WC, Hwang KS, Ding K. An automated detection system for colonoscopy images using a dual encoder-decoder model. Comput Med Imaging Graph. 2020;84:101763. [PMID: 32805673 DOI: 10.1016/j.compmedimag.2020.101763] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Houwen BBSL, Nass KJ, Vleugels JLA, Fockens P, Hazewinkel Y, Dekker E. Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability. Gastrointest Endosc 2023;97:184-199.e16. [PMID: 36084720 DOI: 10.1016/j.gie.2022.08.043] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World J Gastroenterol 2021; 27(38): 6399-6414 [PMID: 34720530 DOI: 10.3748/wjg.v27.i38.6399] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
3 Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27(21): 2681-2709 [PMID: 34135549 DOI: 10.3748/wjg.v27.i21.2681] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
4 Xu J, Zhao R, Yu Y, Zhang Q, Bian X, Wang J, Ge Z, Qian D. Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit. Biomedical Signal Processing and Control 2021;66:102503. [DOI: 10.1016/j.bspc.2021.102503] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]