Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

Topic: Brain imaging, connectivity and assessment

Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

Key findings

• We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features.
• Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy.
• Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs.
• In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively.
• We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1007/s00429-013-0687-3
• PubMed: 24363140
• Original source →

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@article{suk2015-24363140,
  author  = {Suk, H. I. and Lee, S. W. and Shen, D. and {Alzheimer’s Disease Neuroimaging Initiative}},
  title   = {{Latent feature representation with stacked auto-encoder for AD/MCI diagnosis}},
  journal = {Brain structure \& function},
  year    = {2015},
  doi     = {10.1007/s00429-013-0687-3},
  pmid    = {24363140},
  url     = {https://doi.org/10.1007/s00429-013-0687-3}
}

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