報告題目：Variational Bayesian Learning for Medical Imaging data
報 告 人：唐年勝 教授
所在單位：雲南大學
報告時間：2022年7月5日 星期二 下午14:00-15:00
報告地點：騰訊會議 ID：799-408-566 會議密碼：0705
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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 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 approaches 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.
報告人簡介： 唐年勝，雲南大學教授，博士生導師，數學與統計學院院長。“國家傑出青年科學基金”獲得者，教育部“長江學者”特聘教授，教育部“新世紀優秀人才”，國家百千萬人才工程暨有突出貢獻中青年科學家，享受國務院特殊津貼。國際統計學會推薦會員，國際數理統計學會會士，在Journal of the American Statistical Association、Annals of Statistics、Biometrika等學術期刊發表論文170餘篇，出版專著4部。
