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For: Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World J Gastrointest Oncol 2019; 11(12): 1218-1230 [PMID: 31908726 DOI: 10.4251/wjgo.v11.i12.1218]
URL: https://www.wjgnet.com/1948-5182/full/v11/i12/1218.htm
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Nelson S Yee. Machine intelligence for precision oncologyWorld Journal of Translational Medicine 2021; 9(1): 1-10 doi: 10.5528/wjtm.v9.i1.1
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George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway, Neil J Smart, Natalie S Blencowe. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic reviewBMJ Open 2021; 11(10): e054411 doi: 10.1136/bmjopen-2021-054411
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Joseph C Ahn, Touseef Ahmad Qureshi, Amit G Singal, Debiao Li, Ju-Dong Yang. Deep learning in hepatocellular carcinoma: Current status and future perspectivesWorld Journal of Hepatology 2021; 13(12): 2039-2051 doi: 10.4254/wjh.v13.i12.2039
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Yuxiang Wang, Zhongming Huang. High precision detection of small hepatocellular carcinoma using improved EfficientNet with Self-Attention2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS) 2022; : 76 doi: 10.1109/ICIS54925.2022.9882470
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Ching-Juei Yang, Chien-Kuo Wang, Yu-Hua Dean Fang, Jing-Yao Wang, Fong-Chin Su, Hong-Ming Tsai, Yih-Jyh Lin, Hung-Wen Tsai, Lee-Ren Yeh, Khanh N.Q. Le. Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasetsPLOS ONE 2021; 16(8): e0255605 doi: 10.1371/journal.pone.0255605
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Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep LearningInternational Journal of Cloud Applications and Computing 2021; 12(1): 1 doi: 10.4018/IJCAC.2022010109
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Shi Feng, Xiaotian Yu, Wenjie Liang, Xuejie Li, Weixiang Zhong, Wanwan Hu, Han Zhang, Zunlei Feng, Mingli Song, Jing Zhang, Xiuming Zhang. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular CarcinomaFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.762733
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Precilla S Daisy, T. S. Anitha. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?Medical Oncology 2021; 38(5) doi: 10.1007/s12032-021-01500-2
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Vinícius Remus Ballotin, Lucas Goldmann Bigarella, John Soldera, Jonathan Soldera. Deep learning applied to the imaging diagnosis of hepatocellular carcinomaArtificial Intelligence in Gastrointestinal Endoscopy 2021; 2(4): 127-135 doi: 10.37126/aige.v2.i4.127
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22
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23
Wenqi Shi, Sichi Kuang, Sue Cao, Bing Hu, Sidong Xie, Simin Chen, Yinan Chen, Dashan Gao, Yunqiang Chen, Yajing Zhu, Hanxi Zhang, Hui Liu, Meng Ye, Claude B. Sirlin, Jin Wang. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocolAbdominal Radiology 2020; 45(9): 2688 doi: 10.1007/s00261-020-02485-8
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Miguel Jiménez Pérez, Rocío González Grande. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A reviewWorld Journal of Gastroenterology 2020; 26(37): 5617-5628 doi: 10.3748/wjg.v26.i37.5617
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Delia Mitrea, Radu Badea, Paulina Mitrea, Stelian Brad, Sergiu Nedevschi. Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning MethodsSensors 2021; 21(6): 2202 doi: 10.3390/s21062202
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