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Deep Learning-Based Feature Representation for AD/MCI Classification
Heung-Il Suk and Dinggang Shen
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, USAhsuk@med.unc.edu
dgshen@med.unc.edu
Abstract. In recent years, there has been a great interest in computer-aided diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). Unlike the previous methods that consider simple low-level features such as gray matter tissue volumes from MRI, mean signal intensities from PET, in this paper, we propose a deep learning-based feature representation with a stacked auto-encoder. We believe that there exist latent complicated patterns, e.g., non-linear relations, inherent in the low-level features. Combining latent information with the original low-level features helps build a robust model for AD/MCI classification with high diagnostic accuracy. Using the ADNI dataset, we conducted experiments showing that the proposed method is 95.9%, 85.0%, and 75.8% accurate for AD, MCI, and MCI-converter diagnosis, respectively.
LNCS 8150, p. 583 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013