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Multifold Bayesian Kernelization in Alzheimer's Diagnosis

1School of Information Technologies, University of Sydney, Australia.
2Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
3Department of Electronic Engineering, Chinese University of Hong Kong, HK.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
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Pt 2
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 2):303-10.
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Appears in Collections:
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu S., Song Y., Cai W., Pujol S., Kikinis R., Wang X., Feng D. Multifold Bayesian Kernelization in Alzheimer's Diagnosis. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 2):303-10. PMID: 24579154. PMCID: PMC4017205.
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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.

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