Variational Bayesian learning for medical imaging data
报告人：Niansheng Tang (Yunnan University)
Abstract: 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 sources for 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.
About the Speaker:
唐年胜，云南大学教授，数学与统计学院院长。目前主要从事生物统计、贝叶斯统计、高维统计领域及其应用领域的研究，如非随机缺失数据、生存数据分析、高维数据统计推断以及在旅游和健康大数据中的应用研究。“国家杰出青年科学基金”获得者，教育部“长江学者”特聘教授，教育部“新世纪优秀人才”，入选国家百千万人才工程，获国家有突出贡献中青年科学家荣誉，云南省高等学校教学名师。国际统计学会推选会员，国际数理统计学会会士，在Journal of the American Statistical Association、Annals of Statistics、Biomertrika等学术期刊发表论文170余篇，出版专著4部。