title: Multifold Bayesian kernelization in Alzheimer's diagnosis.
abstract: The accurate diagnosis of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) is important in early dementia detection and treatment planning. Most of current studies formulate the AD diagnosis scenario as a classification problem and solve it using various machine learners trained with multi-modal biomarkers. However, the diagnosis accuracy is usually constrained by the performance of the machine learners as well as the methods of integrating the multi-modal data. In this study, we propose a novel diagnosis algorithm, the Multifold Bayesian Kernelization (MBK), which models the diagnosis process as a synthesis analysis of multi-modal biomarkers. MBK constructs a kernel for each biomarker that maximizes the local neighborhood affinity, and further evaluates the contribution of each biomarker based on a Bayesian framework. MBK adopts a novel diagnosis scheme that could infer the subject's diagnosis by synthesizing the output diagnosis probabilities of individual biomarkers. The proposed algorithm, validated using multimodal neuroimaging data from the ADNI baseline cohort with 85 AD, 169 MCI and 77 cognitive normal subjects, achieves significant improvements on all diagnosis groups compared to the state-of-the-art methods.
authors.affiliation: School of Information Technologies, University of Sydney, Australia. sliu7418@uni.sydney.edu.au
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Brigham & Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Sonia
authors.initials: S
authors.lastname: Pujol
authors.affiliation: Brigham & Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.affiliation: Department of Electronic Engineering, Chinese University of Hong Kong, HK.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity