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Cited by in F6Publishing
For: Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep 2021;11:2047. [PMID: 33479370 DOI: 10.1038/s41598-021-81506-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
2 Shad R, Cunningham JP, Ashley EA, Langlotz CP, Hiesinger W. Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat Mach Intell 2021;3:929-35. [DOI: 10.1038/s42256-021-00399-8] [Reference Citation Analysis]
3 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191] [Reference Citation Analysis]
4 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]
5 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]
6 Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R. Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance. Am J Pathol 2021:S0002-9440(21)00165-6. [PMID: 33895120 DOI: 10.1016/j.ajpath.2021.04.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-rokny H; Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR, Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU, Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, IR, Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW, System Administrator, Dibrugarh University, Assam 786004, IN, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran, Omid hospital, Iran University of Medical Sciences, Tehran, IR, FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR, Department of Computer Science, Rutgers University, Camden, NJ 08102, USA, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA, Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA, BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU, Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU. . MBE 2022;19:2381-402. [DOI: 10.3934/mbe.2022110] [Reference Citation Analysis]