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Cited by in F6Publishing
For: Xu J, Wang J, Bian X, Zhu JQ, Tie CW, Liu X, Zhou Z, Ni XG, Qian D. Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy. Laryngoscope 2021. [PMID: 34622964 DOI: 10.1002/lary.29894] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
Number Citing Articles
1 Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 2022;5:184. [PMID: 36539473 DOI: 10.1038/s41746-022-00733-3] [Reference Citation Analysis]
2 Lee VH, Adham M, Ben Kridis W, Bossi P, Chen MY, Chitapanarux I, Gregoire V, Hao SP, Ho C, Ho GF, Kannarunimit D, Kwong DL, Lam KO, Lam WKJ, Le QT, Lee AW, Lee NY, Leung TW, Licitra L, Lim DW, Lin JC, Loh KS, Lou PJ, Machiels JP, Mai HQ, Mesía R, Ng WT, Ngan RK, Tay JK, Tsang RK, Tong CC, Wang HM, Wee JT. International recommendations for plasma Epstein-Barr virus DNA measurement in nasopharyngeal carcinoma in resource-constrained settings: lessons from the COVID-19 pandemic. Lancet Oncol 2022;23:e544-51. [PMID: 36455583 DOI: 10.1016/S1470-2045(22)00505-8] [Reference Citation Analysis]
3 Zhu JQ, Wang ML, Li Y, Zhang W, Li LJ, Liu L, Zhang Y, Han CJ, Tie CW, Wang SX, Wang GQ, Ni XG. Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study. Am J Otolaryngol 2022;44:103695. [PMID: 36473265 DOI: 10.1016/j.amjoto.2022.103695] [Reference Citation Analysis]
4 Ji L, Mao R, Wu J, Ge C, Xiao F, Xu X, Xie L, Gu X. Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks. Diagnostics (Basel) 2022;12:2478. [PMID: 36292167 DOI: 10.3390/diagnostics12102478] [Reference Citation Analysis]
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