Multifold Bayesian Kernelization in Alzheimer’s Diagnosis
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.
KeywordsMild Cognitive Impairment Positron Emission Tomography Data Diagnosis Probability Mild Cognitive Impairment Subject Cognitive Normal
- 4.Singh, N., Wang, A.Y., Sankaranarayanan, P., Fletcher, P.T., Joshi, S.: Genetic, structural and functional imaging biomarkers for early detection of conversion from MCI to AD. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 132–140. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 5.Liu, S., et al.: Neuroimaging biomarker based prediction of Alzheimer’s disease severity with optimized graph construction. In: ISBI 2013, pp. 1324–1327. IEEE (2013)Google Scholar
- 9.Liu, S., Cai, W., et al.: Multi-channel brain atrophy pattern analysis in neuroimaging retrieval. In: ISBI 2013, pp. 206–209. IEEE (2013)Google Scholar
- 17.Pieper, S., Lorensen, B., et al.: The NA-MIC kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community. In: ISBI 2006, pp. 698–701. IEEE (2006)Google Scholar
- 18.Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM TIST 2(3), 27 (2011)Google Scholar