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Computational Intelligence in Biomedical Imaging pp 3–32Cite as

Brain Disease Classification and Progression Using Machine Learning Techniques

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Abstract

In the past two decades, many machine learning techniques have been applied to the detection of neurologic or neuropsychiatric disorders such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), based on different modalities of biomarkers including structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF), etc. This chapter presents some latest developments in application of machine learning tools to AD and MCI diagnosis and progression. We divide our discussions into two parts, pattern classification and pattern regression. We will discuss how the cortical morphological change patterns and the ensemble sparse classifiers can be used for pattern classification and then discuss how the multi-modal multi-task learning (M3T) and the semi-supervised multi-modal relevance vector regression can be applied to pattern regression.

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  1. 1.

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Authors and Affiliations

  1. UNC IDEA Group, Department of Radiology, Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, CB #7513, 130 Mason Farm Road, Chapel Hill, NC, 27599, USA

    Bo Cheng, Chong-Yaw Wee, Manhua Liu, Daoqiang Zhang & Dinggang Shen

  2. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bo Cheng & Daoqiang Zhang

  3. Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Dongchuan Road #800, Shanghai, 200240, China

    Manhua Liu

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  1. Bo Cheng
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  2. Chong-Yaw Wee
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  3. Manhua Liu
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  4. Daoqiang Zhang
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  5. Dinggang Shen
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Correspondence to Dinggang Shen .

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  1. Department of Radiology, University of Chicago, Chicago, Illinois, USA

    Kenji Suzuki

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Cheng, B., Wee, CY., Liu, M., Zhang, D., Shen, D. (2014). Brain Disease Classification and Progression Using Machine Learning Techniques. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_1

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