Multifold Bayesian Kernelization in Alzheimer’s Diagnosis

  • Sidong Liu
  • Yang Song
  • Weidong Cai
  • Sonia Pujol
  • Ron Kikinis
  • Xiaogang Wang
  • Dagan Feng
Conference paper

DOI: 10.1007/978-3-642-40763-5_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)
Cite this paper as:
Liu S. et al. (2013) Multifold Bayesian Kernelization in Alzheimer’s Diagnosis. In: Mori K., Sakuma I., Sato Y., Barillot C., Navab N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg

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 multi-modal 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sidong Liu
    • 1
  • Yang Song
    • 1
  • Weidong Cai
    • 1
  • Sonia Pujol
    • 2
  • Ron Kikinis
    • 2
  • Xiaogang Wang
    • 3
  • Dagan Feng
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneyAustralia
  2. 2.Harvard Medical SchoolBrigham & Women’s HospitalBostonUSA
  3. 3.Department of Electronic EngineeringChinese University of Hong KongHong Kong

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