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Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease

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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 140))

Abstract

Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, which is mild cognitive impairment (MCI). Recently, multi-task feature selection methods are typically used for joint selection of common features across multiple modalities. In this chapter, we review several latest multi-modality feature learning works in diagnoses of AD. Specifically, multi-task feature selection (MTFS) is proposed to jointly select the common subset of relevant features for multiple variables from each modality. Based on MTFS, a manifold regularized multi-task feature learning method (M2TFS) is used to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. However, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. In order to overcome this issue, label-aligned multi-task feature selection (LAMTFS) which can fully explore the realtionships across both modalities and subjects is proposed. Then a discriminative multi-task feature selection method is proposed to select the most discriminative features for multi-modality based classification. The experimental results on the baseline magnetic resonance image (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative (ADNI) data base demonstrate the effectiveness of those above proposed methods.

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Notes

  1. 1.

    https://www.loni.ucla.edu/ADNI.

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Author information

Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing, 211106, People’s Republic of China

    Daoqiang Zhang, Chen Zu & Tingting Ye

  2. Anhui Normal University, 1 Beijingdong Road, Wuhu, 241000, People’s Republic of China

    Biao Jie

Authors
  1. Daoqiang Zhang
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  2. Chen Zu
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  3. Biao Jie
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  4. Tingting Ye
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Correspondence to Daoqiang Zhang .

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Editors and Affiliations

  1. Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

  2. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Yisong Chen

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Zhang, D., Zu, C., Jie, B., Ye, T. (2018). Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease. In: Suzuki, K., Chen, Y. (eds) Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Intelligent Systems Reference Library, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-68843-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-68843-5_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-68843-5

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