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Cited by in F6Publishing
For: Lee KS, Lee E, Choi B, Pyun SB. Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks. Diagnostics (Basel) 2021;11:300. [PMID: 33668528 DOI: 10.3390/diagnostics11020300] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Kolarik M, Sarnovsky M, Paralic J, Babic F. Explainability of deep learning models in medical video analysis: a survey. PeerJ Computer Science 2023;9:e1253. [DOI: 10.7717/peerj-cs.1253] [Reference Citation Analysis]
2 Bandini A, Steele CM. The effect of time on the automated detection of the pharyngeal phase in videofluoroscopic swallowing studies. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021. [DOI: 10.1109/embc46164.2021.9629562] [Reference Citation Analysis]
3 Kim HI, Kim Y, Kim B, Shin DY, Lee SJ, Choi SI. Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network. Diagnostics (Basel) 2021;11:1147. [PMID: 34201839 DOI: 10.3390/diagnostics11071147] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]