title: Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease.
abstract: Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively(1)refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain,(2)updates the intrinsic data representation from the refined subject-wise relationships, and(3)verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
authors.forename: Zhengxia
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Adeli
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Yingying
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Zu
authors.affiliation: School of Computer Science and OPTIMAL Center, Northwestern Polytechnical University, Xi'an 710072, China.
authors.forename: Feiping
authors.initials: F
authors.lastname: Nie
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000473:pathology; D001921:Brain / Q000000981:diagnostic imaging; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging / Q000473:pathology; D018450:Disease Progression; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity