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For: Lequan Yu, Hao Chen, Qi Dou, Jing Qin, Pheng Ann Heng. Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos. IEEE J Biomed Health Inform. 2017;21:65-75. [PMID: 28114049 DOI: 10.1109/jbhi.2016.2637004] [Cited by in Crossref: 99] [Cited by in F6Publishing: 29] [Article Influence: 16.5] [Reference Citation Analysis]
Number Citing Articles
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2 Yoon HJ, Kim JH. Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer. Clin Endosc. 2020;53:127-131. [PMID: 32252505 DOI: 10.5946/ce.2020.046] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
3 Pang X, Zhao Z, Weng Y. The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy. Diagnostics (Basel) 2021;11:694. [PMID: 33919669 DOI: 10.3390/diagnostics11040694] [Reference Citation Analysis]
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8 Owais M, Arsalan M, Mahmood T, Kang JK, Park KR. Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning-Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation. J Med Internet Res 2020;22:e18563. [PMID: 33242010 DOI: 10.2196/18563] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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10 Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27(47): 8103-8122 [DOI: 10.3748/wjg.v27.i47.8103] [Reference Citation Analysis]
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13 Pulido JV, Guleria S, Ehsan L, Shah T, Syed S, Brown DE. SCREENING FOR BARRETT'S ESOPHAGUS WITH PROBE-BASED CONFOCAL LASER ENDOMICROSCOPY VIDEOS. Proc IEEE Int Symp Biomed Imaging 2020;2020:1659-63. [PMID: 34040694 DOI: 10.1109/isbi45749.2020.9098630] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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15 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
16 Guo K, Xu T, Kui X, Zhang R, Chi T. iFusion: Towards efficient intelligence fusion for deep learning from real-time and heterogeneous data. Information Fusion 2019;51:215-23. [DOI: 10.1016/j.inffus.2019.02.008] [Cited by in Crossref: 17] [Article Influence: 5.7] [Reference Citation Analysis]
17 Owais M, Arsalan M, Choi J, Mahmood T, Park KR. Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis. J Clin Med. 2019;8. [PMID: 31284687 DOI: 10.3390/jcm8070986] [Cited by in Crossref: 19] [Cited by in F6Publishing: 12] [Article Influence: 6.3] [Reference Citation Analysis]
18 Xu J, Zhang Q, Yu Y, Zhao R, Bian X, Liu X, Wang J, Ge Z, Qian D. Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection. Comput Methods Programs Biomed 2021;214:106576. [PMID: 34915425 DOI: 10.1016/j.cmpb.2021.106576] [Reference Citation Analysis]
19 Boers T, Putten JV, Struyvenberg M, Fockens K, Jukema J, Schoon E, Sommen FV, Bergman J, With P. Improving Temporal Stability and Accuracy for Endoscopic Video Tissue Classification Using Recurrent Neural Networks. Sensors (Basel) 2020;20:E4133. [PMID: 32722344 DOI: 10.3390/s20154133] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Xu Y, Ding W, Wang Y, Tan Y, Xi C, Ye N, Wu D, Xu X. Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis. PLoS One 2021;16:e0246892. [PMID: 33592048 DOI: 10.1371/journal.pone.0246892] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Chao WL, Manickavasagan H, Krishna SG. Application of Artificial Intelligence in the Detection and Differentiation of Colon Polyps: A Technical Review for Physicians. Diagnostics (Basel). 2019;9. [PMID: 31434208 DOI: 10.3390/diagnostics9030099] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 4.7] [Reference Citation Analysis]
22 Kim KO, Kim EY. Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm. Gut Liver. 2021;15:346-353. [PMID: 32773386 DOI: 10.5009/gnl20186] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Podlasek J, Heesch M, Podlasek R, Kilisiński W, Filip R. Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations. Endosc Int Open 2021;9:E741-8. [PMID: 33937516 DOI: 10.1055/a-1388-6735] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Chen B, Wan J, Chen T, Yu Y, Ji M. A self-attention based faster R-CNN for polyp detection from colonoscopy images. Biomedical Signal Processing and Control 2021;70:103019. [DOI: 10.1016/j.bspc.2021.103019] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26(34): 5090-5100 [PMID: 32982111 DOI: 10.3748/wjg.v26.i34.5090] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
26 Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2(3): 79-88 [DOI: 10.37126/aige.v2.i3.79] [Reference Citation Analysis]
27 Wang S, Xing Y, Zhang L, Gao H, Zhang H. Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization. Comput Math Methods Med. 2019;2019:7546215. [PMID: 31641370 DOI: 10.1155/2019/7546215] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
28 Le A, Salifu MO, McFarlane IM. Artificial Intelligence in Colorectal Polyp Detection and Characterization. Int J Clin Res Trials 2021;6:157. [PMID: 33884326 DOI: 10.15344/2456-8007/2021/157] [Reference Citation Analysis]
29 de Lange T, Halvorsen P, Riegler M. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy. World J Gastroenterol 2018; 24(45): 5057-5062 [PMID: 30568383 DOI: 10.3748/wjg.v24.i45.5057] [Cited by in CrossRef: 15] [Cited by in F6Publishing: 13] [Article Influence: 3.8] [Reference Citation Analysis]
30 Zhou J, Hu N, Huang Z, Song B, Wu C, Zeng F, Wu M. Application of artificial intelligence in gastrointestinal disease: a narrative review. Ann Transl Med 2021;9:1188-1188. [DOI: 10.21037/atm-21-3001] [Reference Citation Analysis]
31 Liu M, Jiang J, Wang Z. Colonic Polyp Detection in Endoscopic Videos With Single Shot Detection Based Deep Convolutional Neural Network. IEEE Access 2019;7:75058-66. [PMID: 33604228 DOI: 10.1109/access.2019.2921027] [Cited by in Crossref: 18] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
32 Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers (Basel). 2019;11:1235. [PMID: 31450799 DOI: 10.3390/cancers11091235] [Cited by in Crossref: 67] [Cited by in F6Publishing: 34] [Article Influence: 22.3] [Reference Citation Analysis]
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34 Min JK, Kwak MS, Cha JM. Overview of Deep Learning in Gastrointestinal Endoscopy. Gut Liver. 2019;13:388-393. [PMID: 30630221 DOI: 10.5009/gnl18384] [Cited by in Crossref: 51] [Cited by in F6Publishing: 37] [Article Influence: 25.5] [Reference Citation Analysis]
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36 Saito H, Tanimoto T, Ozawa T, Ishihara S, Fujishiro M, Shichijo S, Hirasawa D, Matsuda T, Endo Y, Tada T. Automatic anatomical classification of colonoscopic images using deep convolutional neural networks. Gastroenterol Rep (Oxf) 2021;9:226-33. [PMID: 34316372 DOI: 10.1093/gastro/goaa078] [Reference Citation Analysis]
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38 Kim GH, Sung ES, Nam KW. Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network. Biomed Eng Online 2021;20:51. [PMID: 34034766 DOI: 10.1186/s12938-021-00886-4] [Reference Citation Analysis]