Deep learning-based feature representation for AD/MCI classification.

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Suk, H-I; Shen, D

Published Date

  • 2013

Published In

  • Med Image Comput Comput Assist Interv

Volume / Issue

  • 16 / Pt 2

Start / End Page

  • 583 - 590

PubMed ID

  • 24579188

Pubmed Central ID

  • PMC4029347

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-40763-5_72

Conference Location

  • Germany