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Invited Talk “GAN for Medical Image Reconstruction” – Prof. Dr. Jong Chul Ye – Korean Advanced Institute of Science and Technology – Dec 4th 2020, 8:30 AM

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It’s great pleasure to welcome Prof. Dr. Jong Chul Ye from KAIST for a presentation to our lab!

Title: GAN for Medical Image Reconstruction
Date: Dec 4th 2020, 8:30 AM
Location: https://fau.zoom.us/j/95654127882?pwd=WmtFRXVsYS9wR1ViQ1lCVmZYUStHdz09

Abstract: Although deep neural networks have been widely studied for medical imaging applications, most of them are supervised learning framework that requires matched label data. Unfortunately, in many medical imaging applications, high-quality label data is often difficult to obtain, so the need for unsupervised learning is increasing. In this talk, I will mainly focus on unsupervised learning in medical image reconstruction problems such as low-dose X-ray CT, accelerated MRI, ultrasound, optics, etc., when the matched target data are not available. In particular, we introduce a recent advance of generative models, in particular optimal transport-driven CycleGAN framework, which has a strong mathematical background and can be readily incorporated with the imaging physics. The use of optimal transport driven cycleGAN for low-dose X-ray CT, CT metal artifact removal, accelerated MRI, MR motion artifact removal, ultrasound imaging artifact removal, etc., which have been pioneered by our lab, will be also introduced.

Short Bio: Jong Chul Ye is a Professor of the Dept. of Bio/Brain Engineering and Adjunct Professor at Dept. of Mathematical Sciences of Korea Advanced Institute of Science and Technology (KAIST), Korea. He received the B.Sc. and M.Sc. degrees from Seoul National University, Korea, and the Ph.D. from Purdue University, West Lafayette. Before joining KAIST, he was a Senior Researcher at Philips Research, GE Global Research in New York, and a postdoctoral fellow at the University of Illinois at Urbana Champaign. He has served as an associate editor of IEEE Trans. on Image Processing, IEEE Trans. on Computational Imaging, and an editorial board member for Magnetic Resonance in Medicine. He is currently an associate editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine. He is an IEEE Fellow, Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer. He was a General Co-chair for 2020 IEEE Symp. On Biomedical Imaging (ISBI) (with Mathews Jacob). His current research focus is deep learning theory and algorithms for various imaging reconstruction problems in x-ray CT, MRI, optics, ultrasound, remote sensing, etc.