title: Deep learning-based feature representation for AD/MCI classification.
abstract: 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.
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill. hsuk@med.unc.edu
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis*; D001185:Artificial Intelligence; D060825:Cognitive Dysfunction / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity