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Cited by in F6Publishing
For: Xue LY, Jiang ZY, Fu TT, Wang QM, Zhu YL, Dai M, Wang WP, Yu JH, Ding H. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.Eur Radiol. 2020;30:2973-2983. [PMID: 31965257 DOI: 10.1007/s00330-019-06595-w] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 16.0] [Reference Citation Analysis]
Number Citing Articles
1 Ni M, Wang L, Yu H, Wen X, Yang Y, Liu G, Hu Y, Li Z. Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1 -Weighted Imaging: Comparison of Different Radiomics Models. J Magn Reson Imaging 2021;53:1080-9. [PMID: 33043991 DOI: 10.1002/jmri.27391] [Reference Citation Analysis]
2 Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med 2021;128:104115. [PMID: 33227578 DOI: 10.1016/j.compbiomed.2020.104115] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 12.0] [Reference Citation Analysis]
3 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
4 Xue LY, Ding H. Current ultrasound-related strategies for assessing liver fibrosis in chronic liver disease. Chin Med J (Engl) 2020;133:2762-4. [PMID: 33009023 DOI: 10.1097/CM9.0000000000001136] [Reference Citation Analysis]
5 Song KD. Current status of deep learning applications in abdominal ultrasonography. Ultrasonography 2021;40:177-82. [PMID: 33242931 DOI: 10.14366/usg.20085] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
6 Yi J, Kang HK, Kwon JH, Kim KS, Park MH, Seong YK, Kim DW, Ahn B, Ha K, Lee J, Hah Z, Bang WC. Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency. Ultrasonography 2021;40:7-22. [PMID: 33152846 DOI: 10.14366/usg.20102] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
7 Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27(34): 5715-5726 [PMID: 34629796 DOI: 10.3748/wjg.v27.i34.5715] [Reference Citation Analysis]
8 Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021;8:031902. [PMID: 33768134 DOI: 10.1117/1.JMI.8.3.031902] [Reference Citation Analysis]
9 Wang F, Wang Z, Pang L, Wan S, Qiu L. Preparation and in vitro study of stromal cell-derived factor 1-targeted Fe3O4/poly(lactic-co-glycolic acid)/perfluorohexane nanoparticles. Exp Ther Med 2020;20:2003-12. [PMID: 32782510 DOI: 10.3892/etm.2020.8925] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Nowak S, Mesropyan N, Faron A, Block W, Reuter M, Attenberger UI, Luetkens JA, Sprinkart AM. Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning. Eur Radiol 2021. [PMID: 33974149 DOI: 10.1007/s00330-021-07858-1] [Reference Citation Analysis]
11 Chaddad A, Katib Y, Hassan L. Future artificial intelligence tools and perspectives in medicine. Curr Opin Urol 2021;31:371-7. [PMID: 33927099 DOI: 10.1097/MOU.0000000000000884] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver Int 2021. [PMID: 34008300 DOI: 10.1111/liv.14966] [Reference Citation Analysis]
13 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]
14 Zhen SH, Cheng M, Tao YB, Wang YF, Juengpanich S, Jiang ZY, Jiang YK, Yan YY, Lu W, Lue JM, Qian JH, Wu ZY, Sun JH, Lin H, Cai XJ. Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data. Front Oncol. 2020;10:680. [PMID: 32547939 DOI: 10.3389/fonc.2020.00680] [Cited by in Crossref: 21] [Cited by in F6Publishing: 15] [Article Influence: 21.0] [Reference Citation Analysis]