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Brain Structure and Function

, Volume 220, Issue 2, pp 841–859 | Cite as

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

  • Heung-Il Suk
  • Seong-Whan Lee
  • Dinggang ShenEmail author
  • The Alzheimer’s Disease Neuroimaging Initiative
Original Article

Abstract

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

Keywords

Alzheimer’s disease (AD) Mild cognitive impairment (MCI) Multi-modal classification Deep learning Latent feature representation 

Notes

Acknowledgments

This work was supported in part by NIH grants EB006733, EB008374, EB009634, AG041721, MH100217, and AG042599, and also by the National Research Foundation grant (No. 2012-005741) funded by the Korean government.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Heung-Il Suk
    • 1
  • Seong-Whan Lee
    • 2
  • Dinggang Shen
    • 1
    • 2
    Email author
  • The Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Biomedical Research Imaging Center (BRIC) and Department of RadiologyUniversity of North CarolinaChapel HillUSA
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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