Speaker: Prof. Jing Yuan, School of Mathematics and Statistics, Xidian University
Abstract: Many problems of medical image analysis and machine learning 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 recent 30 years, in a rather broad sense. This talk presents an overview of modern dual optimization theory, which delivers both an advanced unified framework of mathematical analysis and high-performance numerical schemes with a wide spectrum of applications. We focus on the optimization problems arising from the most interesting topics: segmentation, registration and reconstruction, and present related analysis and high-performance numerical solutions under the introduced dual optimization perspective.