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

• Published in: Brain Structure and Function Vol. 220; no. 2; pp. 841 - 859
• Main Authors: Suk, Heung-Il, Lee, Seong-Whan, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2015; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - pathology; Alzheimer's disease; Biomedical and Life Sciences; Biomedicine; Brain - pathology; Cell Biology; Cognition & reasoning; Cognitive Dysfunction - pathology; Databases, Factual; Diagnosis, Computer-Assisted - methods; Female; Humans; Male; Medical diagnosis; Methods; Middle Aged; Neural Networks (Computer); Neurology; Neurosciences; Original Article; Deep learning; Latent feature representation; Alzheimer’s disease (AD); Mild cognitive impairment (MCI); Multi-modal classification
• ISSN: 1863-2653; 1863-2661; 1863-2661; 0340-2061
• DOI: 10.1007/s00429-013-0687-3

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.