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. 2013;16(Pt 2):583-90.
doi: 10.1007/978-3-642-40763-5_72.

Deep learning-based feature representation for AD/MCI classification

Heung-Il Suk  1 , Dinggang Shen  2
Affiliations

Deep learning-based feature representation for AD/MCI classification

Heung-Il Suk et al. Med Image Comput Comput Assist Interv. 2013.

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.

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Figures

Fig. 1
Fig. 1
An illustration of the proposed method for AD/MCI diagnosis
Fig. 2
Fig. 2
A deep architecture of our stacked auto-encoder and the two-step parameter optimization scheme
See this image and copyright information in PMC

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