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Cited by in F6Publishing
For: Lee JT, Park E, Jung TD. Automatic Detection of the Pharyngeal Phase in Raw Videos for the Videofluoroscopic Swallowing Study Using Efficient Data Collection and 3D Convolutional Networks . Sensors (Basel) 2019;19:E3873. [PMID: 31500332 DOI: 10.3390/s19183873] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
Number Citing Articles
1 Hsiao MY, Weng CH, Wang YC, Cheng SH, Wei KC, Tung PY, Chen JY, Yeh CY, Wang TG. Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies. Dysphagia 2022. [PMID: 35482213 DOI: 10.1007/s00455-022-10438-0] [Reference Citation Analysis]
2 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 F6Publishing: 2] [Reference Citation Analysis]
3 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: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Lee SJ, Ko JY, Kim HI, Choi S. Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology. Applied Sciences 2020;10:6179. [DOI: 10.3390/app10186179] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
5 Fujinaka A, Mekata K, Takizawa H, Kudo H. Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection. Int J Comput Assist Radiol Surg 2020;15:901-8. [PMID: 32306186 DOI: 10.1007/s11548-020-02145-8] [Reference Citation Analysis]