title: Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.
abstract: Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Zu
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.|School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241000, China.
authors.forename: Biao
authors.initials: B
authors.lastname: Jie
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
authors.forename: Mingxia
authors.initials: M
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
authors.forename: Songcan
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. dgshen@med.unc.edu.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 136-701, Korea. dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. dqzhang@nuaa.edu.cn.
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification* / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D060825:Cognitive Dysfunction / Q000145:classification* / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D018450:Disease Progression; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D064847:Multimodal Imaging / Q000379:methods; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D049268:Positron-Emission Tomography; D011379:Prognosis; D012372:ROC Curve; D019275:Radiopharmaceuticals
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: Alzheimer’s disease; Feature selection; Label alignment; Mild cognitive impairment; Multi-task learning; Multimodal classification