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Published in:
2013 | OriginalPaper | Chapter
Deep Learning-Based Feature Representation for AD/MCI Classification
Authors : Heung-Il Suk, Dinggang Shen
Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Publisher: Springer Berlin Heidelberg
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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.