Brainnetome Lecture Series - Heterogeneous Analysis of Large-scale Medical Imaging Data
Speaker: Prof. Hongtu Zhu, University of North Carolina at Chapel Hill Chair: Prof. Tianzi Jiang, Brainnetome Center, CASIA Time: 14:30-15:30, Feb. 21, 2019 Venue: The 1rd meeting room, 3rd floor, Intelligence Building
Title: Heterogeneous Analysis of Large-scale Medical Imaging Data
Speaker: Prof. Hongtu Zhu, University of North Carolina at Chapel Hill
Chair: Prof. Tianzi Jiang, Brainnetome Center, CASIA
Time: 14:30-15:30, Feb. 21, 2019
Venue: The 1rd meeting room, 3rd floor, Intelligence Building
With the rapid growth of modern technology, many large-scale biomedical studies, e.g., Alzheimer’s disease neuroimaging initiative (ADNI) study, have been conducted to collect massive datasets with large volumes of complex information from increasingly large cohorts. Despite the numerous successes of biomedical studies, the imaging heterogeneity has posed many challenges in both data integration and disease etiology. Specifically, imaging heterogeneity often represents at three different levels: subject level, group level, and study level. This talk mainly focuses on the heterogeneity at study level. The study-level heterogeneity can result from the difference in study environment, population, design, and protocols, which are mostly unknown. Surrogate variable analysis (SVA), which is a powerful tool in tackling this heterogeneity, has been widely used in genomic studies. However, the imaging data is usually represented as functional phenotype while no existing SVA procedures work for functional responses. To address these challenges, a functional latent factor regression model (FLFRM) is proposed to handle the unknown factors. Several inference procedures are established for estimating the unknown parameters and detecting the latent factors. The consistency of estimate of latent variables and the weak convergence of estimate of parameters are systematically investigated. The finite-sample performance of proposed procedures is assessed by Monte Carlo simulations and a real data example on hippocampal surface data from ADNI study.
Dr. Zhu joined DiDi in 2018 from his position of Endowed Bao-Shan Jing Professorship in Diagnostic Imaging and a tenured professor of biostatistics at MD Anderson Cancer Center and a tenured professor of biostatistics at University of North Carolina at Chapel Hill. Dr. Zhu is leading DiDi’s statistical cognitive team with AI scientists and engineers on the development of innovative solutions for the world’s largest transportation platform. Dr. Zhu got his Ph.D. degree in statistics from the Chinese University of Hong Kong in 2000. He is an internationally recognized expert in statistical learning，medical image analysis, precision medicine，biostatistics, artificial intelligence, and big data analytics. He has been an elected Fellow of American Statistical Association and Institute of Mathematical Statistics since 2011. He received an established investigator award from Cancer Prevention & Research Institute of Texas in 2016. He has published more than 250 papers in top journals, including Nature, Nature Neuroscience, PNAS, AOS, and JRSSB. He serves as a chair or area chair of top international conferences including AAAI and Information Processing in Medical Imaging, as well as an editorial board member of premier international journals, including Statistica Sinica, Annals of Statistics, and Journal of American Statistical Association.