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Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
2013
Brain Structure and Function
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
doi:10.1007/s00429-013-0687-3
pmid:24363140
pmcid:PMC4065852
fatcat:ynfewlq3grh5fdhwdjcqbmvg4q