a robust deep model for improved classification of ad mci patients

• DOI: 10.17615/mwwn-g265
• Publication Date: 2015-09-01
• Authors: Li, Feng; Tran, Loc; Thung, Kim-Han; Ji, Shuiwang; Shen, Dinggang; Li, Jiang
• Keywords: Positron emission tomography; Support Vector Machine; Magnetic; 02 engineering and technology; Machine Learning; 03 medical and health sciences; 0302 clinical medicine; Alzheimer Disease; Image Interpretation, Computer-Assisted; 0202 electrical engineering, electronic engineering, information engineering; Humans; Disease; Cognitive Dysfunction; Selection; Biology; Principal Component Analysis; Computer Sciences; Deep learning; 006; Alzheimer's disease; Models, Theoretical; Early diagnosis; Magnetic Resonance Imaging; Representation; Early Diagnosis; Positron-Emission Tomography; Networks; Resonance imaging; Mathematics

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods.