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    Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Bensmail, Halima
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Structural and Functional Bioinformatics Group
    Date
    2013-12
    Permanent link to this record
    http://hdl.handle.net/10754/563113
    
    Metadata
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    Abstract
    Automated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems. © 2013 Elsevier Ltd.
    Citation
    Wang, J. J.-Y., Bensmail, H., & Gao, X. (2013). Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification. Pattern Recognition, 46(12), 3249–3255. doi:10.1016/j.patcog.2013.05.001
    Sponsors
    The study was supported by grants from National Key Laboratory for Novel Software Technology, China (Grant no. KFKT2012B17), 2011 Qatar Annual Research Forum Award (Grant no. ARF2011), and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
    Publisher
    Elsevier BV
    Journal
    Pattern Recognition
    DOI
    10.1016/j.patcog.2013.05.001
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.patcog.2013.05.001
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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