学术报告

【online】 Significant Anatomy Detection Through Sparse Classification: A Comparative Study

发布人:发布时间: 2020-07-23

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题目: Significant Anatomy Detection Through Sparse Classification: A Comparative Study


报告人:Linglong Kong (Department of Mathematical and Statistical Sciences, University of Alberta, Canada)


时间:202081010:00am


报告方式:Zoom会议  ID: 641 6419 1647 (密码: 035895)


摘要: We present a comparative study for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Two types of image-based regularization methods have been proposed in the literature based on either a Graph Net (GN) model or a total variation (TV) model. These studies showed increased classification accuracy and interpretability of results when using image-based regularization, but did not look at the accuracy and quality of the recovered significant regions. In this paper, we theoretically prove bounds on the recovered sparse coefficients and the corresponding selected image regions in four models (two based on GN penalty and two based on TV penalty). Practically, we confirm the theoretical findings by measuring the accuracy of selected regions compared with ground truth on simulated data. We also evaluate the stability of recovered regions over cross-validation folds using real MRI data.Our findings show that the TV penalty is superior to the GN model. In addition, we showed that adding an l2 penalty improves the accuracy of estimated coefficients and selected significant regions for the both types of models. Joint work with Li Zhang, Dana Cobzas and Alan Wilman.


报告人简介:Dr. Linglong Kong is an associate professor at the department of Mathematical and Statistical Sciences of the University of Alberta. He is a Canadian Research Chair in Statistical Learning. He has published more than 40 peer-reviewed manuscripts including top journals AOS, JASA and JRSSB, and top conferences ICML, ICDM, AAAI and IJCAI. Currently, Linglong is serving as associate editors of Journal of the American Statistical Association, International Journal of Imaging Systems and Technology, Canadian Journal of Statistics, member of the Board of the Statistics Society of Canada, the ASA Statistical Imaging Session program chair and the ASA Statistical Computing Session program chair-elect. His research interests include statistical machine learning, high-dimensional data analysis, neuroimaging data analysis, robust statistics and quantile regression.


邀请人: 杨孝平 老师