Deep learning-based feature representation for AD/MCI classification

Topic: Brain imaging, connectivity and assessment

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.

Key findings

• 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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Study details

• Effect size95.9%; 85.0%; 75.8%
• Evidence strengthLow

Read the original

• DOI: 10.1007/978-3-642-40763-5_72
• PubMed: 24579188
• Original source →

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@article{suk2013-24579188,
  author  = {Suk, H. I. and Shen, D.},
  title   = {{Deep learning-based feature representation for AD/MCI classification}},
  journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year    = {2013},
  doi     = {10.1007/978-3-642-40763-5_72},
  pmid    = {24579188},
  url     = {https://doi.org/10.1007/978-3-642-40763-5_72}
}

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