BMSE SEMINAR

Friday, March 23, 2018, 11:00 A.M.

Dasan bldg.109(1_stFloor)

(Host:Prof. Chung, Euiheon/Language:English)

Prof.Jong Chul Ye

KAIST endowed Chair Professor

Professor, Dept. of Bio and Brain Engineering, KAIST

Recently, deep learning approaches have achieved significant performance improvement over existing iterative reconstruction methods in various biomedical image reconstruction problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. In this talk, we first review the current state-of-the art deep learning image reconstruction algorithms for various imaging modality such as x-ray CT, MRI, optical imaging, PET, ultrasound, etc. Then, we also introduce recent thcoretical efforts from signal processing and appllied mathematics which tries to link the deep learning approaches to the classical signal processing approach such as compressed sensing, low-rank matrix completion, wavelets, non-local algorithms, etc. The theoretical understanding so far suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.

Biography

Jong Chul Ye is currently KAIST endowed chair professor and the professor of the Dept. of Bio/Brain Engineering and adjuct professor at Dept.of Mathematical Sciences of 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 worked at Philips Research, and GE Global Research, both in New York. He has served as an Associate Editor of IEEE Trans. on Image Processing, and IEEE Trans. on Computational Imaging. an Editorial Board memeber for magnetic Resonance in Medicine, and International Advisory Board for Physics in Medicine adn Biology. His group was the first place winner of the 2009 Recon Challenge at the International Society for Magnetic Resonance in Medicine (ISMRM) workshop, the second winners at 2016 Low Dose CT Grand Challenge organized by the American Association of Physicists in Medicine (AAPM),and the third place winner for 2017 CVPR NTIRE challenge on example-based single image super-resolution.He was an advisor of student's best paper awards (1st, and 2nd) at 2013 and 2016 IEEE Symp. on Biomedical imaging (ISBI). His current research interests include compressed sensing adn statistical signal processing for various imaging modalities such as MRI, CT, PET, optics, etc.

Integrated Institute of Technology (IIT)/Department of Biomedical Science and Engineering(BMSE)