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Cited by in F6Publishing
For: Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, Zhou X, Li Q, Li J, Wang J, Ma K, Qi J, Hu Z, Tang P, Deng J, Qiu X, Li BY, Shen WD, Quan RP, Yang JT, Huang LY, Xiao Y, Yang ZC, Li Z, Wang SC, Ren H, Liang C, Guo W, Li Y, Xiao H, Gu Y, Yun JP, Huang D, Song Z, Fan X, Chen L, Yan X, Huang ZC, Huang J, Luttrell J, Zhang CY, Zhou W, Zhang K, Yi C, Wu C, Shen H, Wang YP, Xiao HM, Deng HW. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 2021;19:76. [PMID: 33752648 DOI: 10.1186/s12916-021-01942-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Ma L, Little JV, Chen AY, Myers L, Sumer BD, Fei B. Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging. J Biomed Opt 2022;27. [PMID: 35484692 DOI: 10.1117/1.JBO.27.4.046501] [Reference Citation Analysis]
2 Kim D, Lee J, Woo Y, Jeong J, Kim C, Kim D. Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification. JPM 2022;12:136. [DOI: 10.3390/jpm12020136] [Reference Citation Analysis]
3 Alici-karaca D, Akay B, Yay A, Suna P, Nalbantoglu OU, Karaboga D, Basturk A, Balcioglu E, Baran M. A new lightweight convolutional neural network for radiation-induced liver disease classification. Biomedical Signal Processing and Control 2022;73:103463. [DOI: 10.1016/j.bspc.2021.103463] [Reference Citation Analysis]
4 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: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Yu G, Sun K, Xu C, Shi XH, Wu C, Xie T, Meng RQ, Meng XH, Wang KS, Xiao HM, Deng HW. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat Commun 2021;12:6311. [PMID: 34728629 DOI: 10.1038/s41467-021-26643-8] [Reference Citation Analysis]
6 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
7 Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152 [DOI: 10.4251/wjgo.v14.i1.124] [Reference Citation Analysis]
8 Chen ZX, Huang HQ, Wen JY, Qin LS, Song YD, Fang YY, Zeng DT, Huang WJ, Qin XG, Gan TQ, Luo J, Li JJ. Active Enhancer Assessment by H3K27ac ChIP-seq Reveals Claudin-1 as a Biomarker for Radiation Resistance in Colorectal Cancer. Dose Response 2021;19:15593258211058981. [PMID: 34987334 DOI: 10.1177/15593258211058981] [Reference Citation Analysis]
9 Gupta P, Huang Y, Sahoo PK, You JF, Chiang SF, Onthoni DD, Chern YJ, Chao KY, Chiang JM, Yeh CY, Tsai WS. Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning. Diagnostics (Basel) 2021;11:1398. [PMID: 34441332 DOI: 10.3390/diagnostics11081398] [Reference Citation Analysis]
10 Hatzidaki E, Iliopoulos A, Papasotiriou I. A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning. Entropy (Basel) 2021;23:1248. [PMID: 34681972 DOI: 10.3390/e23101248] [Reference Citation Analysis]