title: Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease.
abstract: Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
authors.affiliation: Key Laboratory of Advanced Network and Intellectual Technology, Chongqing Three Gorges University, Chongqing, 404120, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Cheng
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.|Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
authors.forename: Mingxia
authors.initials: M
authors.lastname: Liu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA. dgshen@med.unc.edu.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea. dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China.
authors.forename: Zuoyong
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. dqzhang@nuaa.edu.cn.|Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China. dqzhang@nuaa.edu.cn.
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000000981:diagnostic imaging* / Q000523:psychology*; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging* / Q000209:etiology; D016208:Databases, Factual / Q000706:statistics & numerical data; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D014163:Transfer, Psychology
keywords: Alzheimer’s disease (AD); Feature selection; Multi-domain; Transfer learning