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Decision Forests for Computer Vision and Medical Image Analysis pp 261–272Cite as

Manifold Forests for Multi-modality Classification of Alzheimer’s Disease

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Neurodegenerative disorders, such as Alzheimer’s disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. This chapter describes a framework within which a supervised version of manifold forests is used to perform multi-modality classification of patients with Alzheimer’s disease, patients with mild cognitive impairment, and elderly cognitively normal individuals. In this chapter, manifold forests are used to derive supervised similarity measures, with the aim of generating manifolds that are optimal for the task of clinical group discrimination. Embeddings are thus learned from labeled training data and used to infer the clinical labels of test data mapped into this space. Similarities from multiple (image- and non-image-based) modalities are combined to generate an embedding that simultaneously encodes information from all diverse features. Multi-modality classification is performed using coordinates from this joint embedding. Manifold forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data.

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Notes

  1. 1.

    http://cran.r-project.org/web/packages/randomForest.

  2. 2.

    http://adni.loni.ucla.edu.

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Author information

Authors and Affiliations

  1. Imperial College London, London, UK

    K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers & D. Rueckert

  2. King’s College London, London, UK

    P. Aljabar

  3. Fondation Neurodis, Lyon, France

    R. A. Heckemann & A. Hammers

Authors
  1. K. R. Gray
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  2. P. Aljabar
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  3. R. A. Heckemann
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  4. A. Hammers
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  5. D. Rueckert
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Editor information

Editors and Affiliations

  1. Microsoft Research Ltd., 7 J.J. Thomson Avenue, Cambridge, CB3 0FB, United Kingdom

    A. Criminisi

  2. Microsoft Research Ltd., 7 J.J. Thomson Avenue, Cambridge, CB3 0FB, United Kingdom

    J. Shotton

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Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D. (2013). Manifold Forests for Multi-modality Classification of Alzheimer’s Disease. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_18

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  • DOI: https://doi.org/10.1007/978-1-4471-4929-3_18

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4928-6

  • Online ISBN: 978-1-4471-4929-3

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