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Conference Paper Detection of the Pharyngeal Phase in the Videofluoroscopic Swallowing Study Using Inflated 3D Convolutional Networks
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Authors
Jong Taek Lee, Eunhee Park
Issue Date
2018-09
Citation
International Workshop on Machine Learning in Medical Imaging (MLMI) 2018 (LNCS 11046), pp.328-336
Publisher
Springer
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1007/978-3-030-00919-9_38
Abstract
Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. Previous computer assisted analysis of VFSS required manual preparation to mark several anatomical structures and to select time intervals of interest such as a pharyngeal phase during swallowing. These processes were still costly and challenging for clinicians. In this study, we present a novel approach to detect the pharyngeal phase of swallowing through whole of VFSS video clips using Inflated 3D Convolutional Networks (I3D) without additional manual annotations.
KSP Keywords
3D convolutional networks, Computer Assisted Analysis, Diagnostic tool, Novel approach, Time interval, Video clips