caa a22 4500 606203508 CHVBK 20210128100958.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00429-013-0687-3 doi (NATIONALLICENCE)springer-10.1007/s00429-013-0687-3 Latent feature representation with stacked auto-encoder for AD/MCI diagnosis [Elektronische Daten] [Heung-Il Suk, Seong-Whan Lee, Dinggang Shen] 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. Springer-Verlag Berlin Heidelberg, 2013 Alzheimer's disease (AD) nationallicence Mild cognitive impairment (MCI) nationallicence Multi-modal classification nationallicence Deep learning nationallicence Latent feature representation nationallicence Suk Heung-Il Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, 27599, Chapel Hill, NC, USA aut Lee Seong-Whan Department of Brain and Cognitive Engineering, Korea University, 136-713, Seoul, Republic of Korea aut Shen Dinggang Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, 27599, Chapel Hill, NC, USA aut Brain Structure and Function Springer Berlin Heidelberg 220/2(2015-03-01), 841-859 1863-2653 220:2<841 2015 220 429 https://doi.org/10.1007/s00429-013-0687-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00429-013-0687-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Suk Heung-Il Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, 27599, Chapel Hill, NC, USA aut NATIONALLICENCE 700 1- Lee Seong-Whan Department of Brain and Cognitive Engineering, Korea University, 136-713, Seoul, Republic of Korea aut NATIONALLICENCE 700 1- Shen Dinggang Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, 27599, Chapel Hill, NC, USA aut NATIONALLICENCE 773 0- Brain Structure and Function Springer Berlin Heidelberg 220/2(2015-03-01), 841-859 1863-2653 220:2<841 2015 220 429