title: Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.
abstract: Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.
authors: Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors: Biao
authors: B
authors: Jie
authors: Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors: Daoqiang
authors: D
authors: Zhang
authors: Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors: Bo
authors: B
authors: Cheng
authors: Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors: Dinggang
authors: D
authors: Shen
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis*; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique