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High-Performance Dual-ADMM Optimization Theory with Applications to Medical Image Analysis

Source:   Author:  Date:2019-12-26  ClickTimes:

SpeakerJing Yuan


Many problems of medical image analysis are challenging due to the associated complex optimization formulations and constraints, extremely big image data being processed, poor imaging quality, missing data etc. On the other hand, it is highly desired to process and analyze the acquired imaging data, for example segmentation and registration etc., in an automated and efficient numerical way, which motivated vast active studies during the last 30 years, in a rather broad sense. This talk targets to present an overview of modern dual optimization theory, which delivers an advanced unified framework of mathematical analysis and high-performance ADMM numerical schemes along with a wide spectrum of applications. We focus on the optimization problems arising from the most interesting topics:segmentation and registration, and present both analysis and high-performance numerical solutions in a unified manner in terms of dual optimization.

Time: December27th9:30

Venue:Lecture Hall,Beiyan, School of Mathematics and Statistics

Speaker Introduction:

Jing Yuan is a Distinguished Professor of Xidian University. He graduated from the School of Physics of Peking University , received his doctor's degree from the School of Mathematics and Computer Science of Heidelberg University in Germany, postdoctoral degree from University of Western Ontario, Canada. He was a researcher at the Robarts Research Institute in Canada. Now, he is a Visiting Professor at the University of Quebec and the South University of Science and Technology of China. His main research fields are optimization theory and algorithm, the realization of parallel fast optimization algorithm and its calculation, computer medical image analysis, computer vision and machine learning related applications. He has published nearly 100 papers in IJCV,CVPR,ECCV, ICML, IEEE TIP, IEEE PAMI and other top academic journals.