报告题目：Variational Bayesian Learning for Medical Imaging data

报告人：唐年胜（云南大学数学与统计学院，教授）

报告时间：2022年7月4日 14:00-15:30

报告地点：腾讯会议ID：836-520-514

校内联系人：李天然

报告摘要：With the recently developed medical imaging technology, brain images are captured through various scanners. Magnetic resonance image (MRI) and function magnetic resonance image (fMRI) are two widely-used imaging data sourcesfor studying brain disease. In disease diagnosis study, disease prediction based on MRI and fMRI data has received considerable attention over the past years. A key challenging in analyzing MRI and fMRI data is to alleviate the well-known curse of dimensionality. Many Bayesian methods have been developed to address the issue. This paper aims to introduce variational Bayesian approches to explore the relationship between regions of interest (ROIs) and some specified disease based on high-dimensional generalized linear models, ultrahigh-dimensional generalized tensor regression models,and high-dimensional gaussian graphical models. Some examples associated with MRI and fMRI data analysis are illustrated.