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【医学部学术讲座】——From Model Optimization to Interpretable and Coll

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主讲嘉宾:樊鑫 教授 大连理工大学

时间:20181213日周四上午10:00-11:30

地点:医学院A6-811会议室

主持人:雷柏英

内容简介:Model optimization plays the key role in many learning and vision tasks. However, designing numerical schemes always need high mathematical skills and rich domain knowledge. Moreover, it is always challenging to apply the generally designed iterations in specific real-world scenarios. In this talk, we introduce a series of paradigms to design task-specific optimization schemes based on inexact learnable architectures. The theoretical properties of these deeply trained propagations are carefully investigated. We demonstrate that we actually provide a new way to establish interpretable and collaborative deep learning models for different real-world applications. Comparisons to adversarial mechanisms in GAN will also be covered. Finally, we demonstrate how to apply the proposed framework to address MRI reconstruction. By optimizing a compressed sensing energy minimization formulation, we design a coupled deep model to simultaneously reconstruct MRI images and remove Rician noise, resulting in a robust medical imaging reconstruction.

专家简历:Dr. Xin Fan is currently Professor of Dalian University of Technology, and Dean of School of DUT-RU Information Science and Engineering. He received the B.E. and Ph.D. degrees in information and communication engineering from Xian Jiaotong University, Xian, China, in 1998 and 2004, respectively. He spent three years for postdoc training in US. He joined the School of Software, Dalian University of University, in 2009. His current research interests include computational geometry and machine learning, and their applications to image processing and analysis. He has published over 100 papers in top journals and conferences including IEEE TIP, TMM, NIPS, ICCV, ECCV, ACM MM, AAAI and IJCAI. He has been the PI of four grants from NSF of China including one key project. He won the best student paper award at ICME2015 as the corresponding author and was among the shortlist of the best paper awards at ICIP2013, ICIP2015 and ICME2017.

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