12 Publications by "T. E. Lever"


T. E. Lever, A. M. Kloepper, I. Deninger, A. Hamad, B. L. Hopewell, A. K. Ovaitt, M. Szewczyk, F. Bunyak, B. Zitsch, B. Blake, and others

Advancing laryngeal adductor reflex testing beyond sensory threshold detection

Dysphagia, pgs. 1--21, 2021

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Y. Y. Wang, K. Gao, A. Hamad, B. McCarthy, A. M. Kloepper, T. E. Lever, and F. Bunyak

Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning

5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 2021

oral diadochokinesis, syllable detection, mouth/jaw motion, deep learning

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Y. Y. Wang, A. S. Hamad, T. E. Lever, and F. Bunyak

Orthogonal Region Selection Network for Laryngeal Closure Detection in Laryngoscopy Videos

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pgs. 2167-2172, 2020

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L. Welby, H. Caudill, G. Yitsege, A. Hamad, F. Bunyak, I. E. Zohn, T. Maynard, A.-S. LaMantia, D. Mendelowitz, and T. E. Lever

Persistent feeding and swallowing deficits in a mouse model of 22q11. 2 deletion syndrome

Frontiers in Neurology, pgs. 4, 2020

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M. M. Haney, A. Hamad, H. G. Woldu, M. Ciucci, N. Nichols, F. Bunyak, and T. E. Lever

Recurrent laryngeal nerve transection in mice results in translational upper airway dysfunction

Journal of Comparative Neurology, Volume 528, pgs. 574--596, 2020

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A. S. Hamad, Y. Y. Wang, T. E. Lever, and F. Bunyak

Ensemble of deep cascades for detection of laryngeal adductor reflex events in Endoscopy Videos

IEEE International Conference on Image Processing (ICIP), pgs. 300-304, 2020

deep learning, laryngeal adductor reflex, automated detection, endoscopy video analysis

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#288: G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. E. Lever, and F. Bunyak

Facial expression recognition for monitoring neurological disorders based on convolutional neural network

Multimedia Tools and Applications, Volume 78, pgs. 31581-31603, 2019

facial component segmentation, facial expression recognition, convolutional neural network, deep learning

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M. M. Haney, A. Hamad, E. Leary, F. Bunyak, and T. E. Lever

Automated quantification of vocal fold motion in a recurrent laryngeal nerve injury mouse model

The Laryngoscope, Volume 129, pgs. E247--E254, 2019

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A. Hamad, M. Haney, T. E. Lever, and F. Bunyak

Automated segmentation of the vocal folds in laryngeal endoscopy videos using deep convolutional regression networks

IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pgs. 140--148, 2019

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Y. Y. Wang, K. Gao, A. M. Kloepper, Y. Zhao, M. Kuruvilla-Dugdale, T. E. Lever, and F. Bunyak

DeepDDK: A deep learning based oral-diadochokinesis analysis software

IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pgs. 1--4, 2019

diadochokinesis analysis, speech signal analysis, deep learning, event detection, event localization

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#280: F. Bunyak, N. Shiraishi, K. Palaniappan, T. E. Lever, L. Avivi-Arber, and K. Takahashi

Development of semi-automatic procedure for detection and tracking of fiducial markers for orofacial kinematics during natural feeding

In Engineering in Medicine and Biology Society (EMBC), pgs. 580-583, 2018

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#259: G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. Lever, and F. Bunyak

Deep learning-based facial expression recognition for monitoring neurological disorders

EEE International Conference on Bioinformatics and Biomedicine (BIBM) , pgs. 1652-1657, 2017

convolutional neural networks, deep learning, facial component segmentation, facial expression recognition

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