title: Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging.
abstract: Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume. We measured the statistical uncertainty related to these spatial features via transformation using an incomplete random forest and formulated the MCI identification problem under a robust optimization framework. We compared our approach to other state-of-the-art methods in different learning schemas. Our method outperformed the other techniques in the ability to separate MCI from normal controls.
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia.
authors.forename: Shen
authors.initials: S
authors.lastname: Lu
authors.affiliation: Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: yxia@nwpu.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. Electronic address: tom.cai@sydney.edu.au.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Department of Molecular Imaging, Royal Prince Alfred Hospital, NSW 2050, Australia; Sydney Medical School, University of Sydney, NSW 2006, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Fulham
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia; Med-X Research Institute, Shanghai Jiaotong University, Shanghai 200030, China.
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000000981:diagnostic imaging; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008297:Male; D049268:Positron-Emission Tomography; D062706:Prodromal Symptoms; D060388:Support Vector Machine; D035501:Uncertainty
chemical_list: D019788:Fluorodeoxyglucose F18
keywords: Alzheimer’s disease; FDG-PET; Mild cognitive impairment; Robust optimization