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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] [Cited by in CrossRef: 43] [Cited by in F6Publishing: 34] [Article Influence: 14.3] [Reference Citation Analysis]
Number Citing Articles
1 Daisy PS, Anitha TS. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy? Med Oncol 2021;38:53. [PMID: 33811540 DOI: 10.1007/s12032-021-01500-2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Ponnoprat D, Inkeaw P, Chaijaruwanich J, Traisathit P, Sripan P, Inmutto N, Na Chiangmai W, Pongnikorn D, Chitapanarux I. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.Med Biol Eng Comput. 2020;58:2497-2515. [PMID: 32794015 DOI: 10.1007/s11517-020-02229-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
3 Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135 [DOI: 10.37126/aige.v2.i4.127] [Reference Citation Analysis]
4 Tran S, Cheng C, Liu D. A Multiple Layer U-Net, U n -Net, for Liver and Liver Tumor Segmentation in CT. IEEE Access 2021;9:3752-64. [DOI: 10.1109/access.2020.3047861] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 12.0] [Reference Citation Analysis]
5 Zhang J, Huang S, Xu Y, Wu J. Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022;12:763842. [PMID: 35280776 DOI: 10.3389/fonc.2022.763842] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Fehrenbach U, Xin S, Hartenstein A, Auer TA, Dräger F, Froböse K, Jann H, Mogl M, Amthauer H, Geisel D, Denecke T, Wiedenmann B, Penzkofer T. Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making. Cancers (Basel) 2021;13:2726. [PMID: 34072865 DOI: 10.3390/cancers13112726] [Reference Citation Analysis]
7 Anil B. C., Dayananda P., Nethravathi B., Raisinghani MS. Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning: . International Journal of Cloud Applications and Computing 2022;12:1-13. [DOI: 10.4018/ijcac.2022010109] [Reference Citation Analysis]
8 Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. Medicina (Kaunas) 2022;58:459. [PMID: 35454298 DOI: 10.3390/medicina58040459] [Reference Citation Analysis]
9 Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol (NY) 2021;46:216-25. [PMID: 32500237 DOI: 10.1007/s00261-020-02604-5] [Cited by in Crossref: 23] [Cited by in F6Publishing: 13] [Article Influence: 23.0] [Reference Citation Analysis]
10 Xu Q. Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network. Comput Intell Neurosci 2022;2022:1181189. [PMID: 35528371 DOI: 10.1155/2022/1181189] [Reference Citation Analysis]
11 Feng S, Yu X, Liang W, Li X, Zhong W, Hu W, Zhang H, Feng Z, Song M, Zhang J, Zhang X. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma. Front Oncol 2021;11:762733. [DOI: 10.3389/fonc.2021.762733] [Reference Citation Analysis]
12 Nisa M, Buzdar SA, Khan K, Ahmad MS. Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images. Symmetry 2022;14:383. [DOI: 10.3390/sym14020383] [Reference Citation Analysis]
13 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]
14 Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021;11:698373. [PMID: 34616673 DOI: 10.3389/fonc.2021.698373] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Cui J, Wang H. Algorithm of generating music melody based on single-exposure high dynamic range digital image using convolutional neural network. J Electron Imag 2022;31. [DOI: 10.1117/1.jei.31.5.051417] [Reference Citation Analysis]
16 Lai Q, Spoletini G, Mennini G, Larghi Laureiro Z, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26(42): 6679-6688 [PMID: 33268955 DOI: 10.3748/wjg.v26.i42.6679] [Cited by in CrossRef: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
17 Amin J, Anjum MA, Sharif M, Kadry S, Nadeem A, Ahmad SF. Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks. Diagnostics 2022;12:823. [DOI: 10.3390/diagnostics12040823] [Reference Citation Analysis]
18 Phan DV, Chan CL, Li AA, Chien TY, Nguyen VC. Liver cancer prediction in a viral hepatitis cohort: A deep learning approach. Int J Cancer 2020;147:2871-8. [PMID: 32761609 DOI: 10.1002/ijc.33245] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
20 Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021;11:1194. [PMID: 34209197 DOI: 10.3390/diagnostics11071194] [Reference Citation Analysis]
21 Feng Q, Chen H, Jiang R. Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocessors and Microsystems 2021;87:104387. [DOI: 10.1016/j.micpro.2021.104387] [Reference Citation Analysis]
22 Shi W, Kuang S, Cao S, Hu B, Xie S, Chen S, Chen Y, Gao D, Chen Y, Zhu Y, Zhang H, Liu H, Ye M, Sirlin CB, Wang J. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdom Radiol (NY) 2020;45:2688-97. [PMID: 32232524 DOI: 10.1007/s00261-020-02485-8] [Cited by in Crossref: 16] [Cited by in F6Publishing: 12] [Article Influence: 8.0] [Reference Citation Analysis]
23 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Yee NS. Machine intelligence for precision oncology. World J Transl Med 2021; 9(1): 1-10 [DOI: 10.5528/wjtm.v9.i1.1] [Reference Citation Analysis]
25 Kumar Y, Gupta S, Singla R, Hu YC. A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. Arch Comput Methods Eng 2021;:1-28. [PMID: 34602811 DOI: 10.1007/s11831-021-09648-w] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Fowler GE, Macefield RC, Hardacre C, Callaway MP, Smart NJ, Blencowe NS. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review. BMJ Open 2021;11:e054411. [PMID: 34670769 DOI: 10.1136/bmjopen-2021-054411] [Reference Citation Analysis]
27 Mitrea D, Badea R, Mitrea P, Brad S, Nedevschi S. Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods. Sensors (Basel) 2021;21:2202. [PMID: 33801125 DOI: 10.3390/s21062202] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Zhu Y, Yu A, Rong H, Wang D, Song Y, Liu Z, Sheng VS. Multi-Resolution Image Segmentation Based on a Cascaded U-ADenseNet for the Liver and Tumors. J Pers Med 2021;11:1044. [PMID: 34683185 DOI: 10.3390/jpm11101044] [Reference Citation Analysis]
29 Chen C, Chen C, Ma M, Ma X, Lv X, Dong X, Yan Z, Zhu M, Chen J. Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism. BMC Med Inform Decis Mak 2022;22:176. [PMID: 35787805 DOI: 10.1186/s12911-022-01919-1] [Reference Citation Analysis]
30 Dillman JR, Somasundaram E, Brady SL, He L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol 2021. [PMID: 33844048 DOI: 10.1007/s00247-021-05057-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
31 Khan RA, Luo Y, Wu F. Machine learning based liver disease diagnosis: A systematic review. Neurocomputing 2022;468:492-509. [DOI: 10.1016/j.neucom.2021.08.138] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
32 Yang CJ, Wang CK, Fang YD, Wang JY, Su FC, Tsai HM, Lin YJ, Tsai HW, Yeh LR. 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 datasets. PLoS One 2021;16:e0255605. [PMID: 34375365 DOI: 10.1371/journal.pone.0255605] [Reference Citation Analysis]
33 Alaraimi S, Okedu KE, Tianfield H, Holden R, Uthmani O. Transfer learning networks with skip connections for classification of brain tumors. Int J Imaging Syst Technol 2021;31:1564-82. [DOI: 10.1002/ima.22546] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
34 Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. Journal of Clinical and Experimental Hepatology 2022. [DOI: 10.1016/j.jceh.2022.06.009] [Reference Citation Analysis]
35 Fiz F, Viganò L, Gennaro N, Costa G, La Bella L, Boichuk A, Cavinato L, Sollini M, Politi LS, Chiti A, Torzilli G. Radiomics of Liver Metastases: A Systematic Review. Cancers (Basel) 2020;12:E2881. [PMID: 33036490 DOI: 10.3390/cancers12102881] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 5.5] [Reference Citation Analysis]