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Cited by in F6Publishing
For: Shi JY, Wang X, Ding GY, Dong Z, Han J, Guan Z, Ma LJ, Zheng Y, Zhang L, Yu GZ, Wang XY, Ding ZB, Ke AW, Yang H, Wang L, Ai L, Cao Y, Zhou J, Fan J, Liu X, Gao Q. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 2021;70:951-61. [PMID: 32998878 DOI: 10.1136/gutjnl-2020-320930] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2(6): 104-114 [DOI: 10.35711/aimi.v2.i6.104] [Reference Citation Analysis]
2 Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100443] [Reference Citation Analysis]
3 Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [DOI: 10.4254/wjh.v13.i12.2039] [Reference Citation Analysis]
4 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Reference Citation Analysis]
5 Kubota N, Fujiwara N, Hoshida Y. Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk. J Clin Med 2020;9:E3843. [PMID: 33256232 DOI: 10.3390/jcm9123843] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021;9:159. [PMID: 33562077 DOI: 10.3390/biomedicines9020159] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Wang X, Chen Y, Gao Y, Zhang H, Guan Z, Dong Z, Zheng Y, Jiang J, Yang H, Wang L, Huang X, Ai L, Yu W, Li H, Dong C, Zhou Z, Liu X, Yu G. Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun 2021;12:1637. [PMID: 33712598 DOI: 10.1038/s41467-021-21674-7] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
8 Yang L, Fan X, Qin W, Xu Y, Zou B, Fan B, Wang S, Dong T, Wang L. A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer. Cancer Med 2022. [PMID: 35491970 DOI: 10.1002/cam4.4782] [Reference Citation Analysis]
9 Philips CA, Rajesh S, Nair DC, Ahamed R, Abduljaleel JK, Augustine P. Hepatocellular Carcinoma in 2021: An Exhaustive Update. Cureus 2021;13:e19274. [PMID: 34754704 DOI: 10.7759/cureus.19274] [Reference Citation Analysis]
10 Chu CS, Lee NP, Ho JWK, Choi SW, Thomson PJ. Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review. JAMA Otolaryngol Head Neck Surg 2021;147:893-900. [PMID: 34410314 DOI: 10.1001/jamaoto.2021.2028] [Reference Citation Analysis]
11 Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther 2021;6:312. [PMID: 34417437 DOI: 10.1038/s41392-021-00729-7] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Fu S, Lai H, Huang M, Li Q, Liu Y, Zhang J, Huang J, Chen X, Duan C, Li X, Wang T, He X, Yan J, Lu L. Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma. EClinicalMedicine 2021;42:101201. [PMID: 34917908 DOI: 10.1016/j.eclinm.2021.101201] [Reference Citation Analysis]
13 Chen W, Fu M, Zhang C, Xing Q, Zhou F, Lin M, Dong X, Huang J, Lin S, Hong M, Zheng Q, Pan J. Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond. Front Med 2022;9:853261. [DOI: 10.3389/fmed.2022.853261] [Reference Citation Analysis]