title: Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder.
abstract: In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals.
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.|IDEA Lab, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
authors.forename: Liye
authors.initials: L
authors.lastname: Wang
authors.affiliation: IDEA Lab, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Tang
authors.affiliation: IDEA Lab, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.affiliation: IDEA Lab, Department of Radiology and Biomedical Research Imaging Center (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, Republic of Korea. dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000067877:Autism Spectrum Disorder / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D002648:Child; D066264:Datasets as Topic; D005260:Female; D066128:Gray Matter / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012372:ROC Curve; D060388:Support Vector Machine; D066127:White Matter / Q000000981:diagnostic imaging
keywords: Diagnosis of autism spectrum disorder; Magnetic resonance imaging (MRI); Multi-task feature selection