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    Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

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    Author
    Woo, J.
    Xing, F.
    Prince, J.L.
    Date
    2019
    Journal
    Journal of the Acoustical Society of America
    Publisher
    Acoustical Society of America
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1121/1.5103191
    Abstract
    The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders. Copyright 2019 Acoustical Society of America.
    Keyword
    tagged-MRI
    Tongue--Cancer
    Tongue
    Rehabilitation of Speech and Language Disorders
    Deep Learning
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066103401&doi=10.1121%2f1.5103191&partnerID=40&md5=91203651c52ca1d11622dccb66a58ac3; http://hdl.handle.net/10713/10604
    ae974a485f413a2113503eed53cd6c53
    10.1121/1.5103191
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