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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 82–90Cite as

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Domain Transfer Learning for MCI Conversion Prediction

Domain Transfer Learning for MCI Conversion Prediction

  • Bo Cheng19,20,
  • Daoqiang Zhang19,20 &
  • Dinggang Shen20 
  • Conference paper
  • 5916 Accesses

  • 24 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

In recent studies of Alzheimer’s disease (AD), it has increasing attentions in identifying mild cognitive impairment (MCI) converters (MCI-C) from MCI non-converters (MCI-NC). Note that MCI is a prodromal stage of AD, with possibility to convert to AD. Most traditional methods for MCI conversion prediction learn information only from MCI subjects (including MCI-C and MCI-NC), not from other related subjects, e.g., AD and normal controls (NC), which can actually aid the classification between MCI-C and MCI-NC. In this paper, we propose a novel domain-transfer learning method for MCI conversion prediction. Different from most existing methods, we classify MCI-C and MCI-NC with aid from the domain knowledge learned with AD and NC subjects as auxiliary domain to further improve the classification performance. Our method contains two key components: (1) the cross-domain kernel learning for transferring auxiliary domain knowledge, and (2) the adapted support vector machine (SVM) decision function construction for cross-domain and auxiliary domain knowledge fusion. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database show that the proposed method can significantly improve the classification performance between MCI-C and MCI-NC, with aid of domain knowledge learned from AD and NC subjects.

Keywords

  • Mild Cognitive Impairment
  • Target Domain
  • Mild Cognitive Impairment Patient
  • Normal Control Subject
  • Mild Cognitive Impairment Subject

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Bo Cheng & Daoqiang Zhang

  2. Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27599, USA

    Bo Cheng, Daoqiang Zhang & Dinggang Shen

Authors
  1. Bo Cheng
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  2. Daoqiang Zhang
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  3. Dinggang Shen
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Cheng, B., Zhang, D., Shen, D. (2012). Domain Transfer Learning for MCI Conversion Prediction. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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