A0999
Title: Bayesian hierarchical model for patient-specific abnormal region detection
Authors: Rongjie Liu - University of Georgia (United States) [presenting]
Abstract: Early detection of Alzheimer's disease (AD) can help in better management of the disease and delaying the disease progression. In this study, a Bayesian-based approach, i.e., PARD (patient-specific abnormal region detection), is proposed to detect patient-specific diseased regions in AD studies. The Bayesian hierarchical model used for detecting diseased regions is formulated, all the prior distributions related to the model parameters, as well as hyperparameters, are specified, and the joint posterior distributions are given. The algorithm is followed for sampling the parameters and hyperparameters from the joint posterior distribution, and derivations of the full conditional distributions and joint probability calculations are required for executing the sampling algorithm. Finally, the effectiveness of the proposed algorithm is compared with some other popular methods on the simulated data, and the performance is demonstrated on real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
