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Cited by in F6Publishing
For: Yang J, Chang L, Li S, He X, Zhu T. WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding. Int J Comput Assist Radiol Surg. 2020;15:1291-1302. [PMID: 32447521 DOI: 10.1007/s11548-020-02190-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 2.3] [Reference Citation Analysis]
Number Citing Articles
1 Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med 2022;9. [DOI: 10.3389/fmed.2022.1018937] [Reference Citation Analysis]
2 Su Q, Wang F, Chen D, Chen G, Li C, Wei L. Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases. Computers in Biology and Medicine 2022;150:106054. [DOI: 10.1016/j.compbiomed.2022.106054] [Reference Citation Analysis]
3 Li S, Yao J, Cao J, Kong X, Zhu J. Effective high-to-low-level feature aggregation network for endoscopic image classification. Int J Comput Assist Radiol Surg 2022. [PMID: 35568744 DOI: 10.1007/s11548-022-02591-6] [Reference Citation Analysis]
4 Lai Z, Jia Z. Multi-lesion classification of WCE images based on deep sparse feature selection and feature fusion. 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) 2022. [DOI: 10.1109/iwecai55315.2022.00095] [Reference Citation Analysis]
5 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: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
6 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
7 Vasilakakis M, Sovatzidi G, Iakovidis DK. Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images Based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 2021. [DOI: 10.1007/978-3-030-87199-4_46] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Fu Z, Jin Z, Zhang C, He Z, Zha Z, Hu C, Gan T, Yan Q, Wang P, Ye X. The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology. IEEE Access 2021;9:41144-67. [DOI: 10.1109/access.2021.3065104] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
9 Öztürk Ş, Özkaya U. Residual LSTM layered CNN for classification of gastrointestinal tract diseases. J Biomed Inform 2021;113:103638. [PMID: 33271341 DOI: 10.1016/j.jbi.2020.103638] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 4.7] [Reference Citation Analysis]