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

Title: GAN for Medical Image Reconstruction
Prof. Dr. Jong Chul Ye, KAIST

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.

References

Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN
S Yang, EY Kim, JC Ye
arXiv preprint arXiv:2011.13150 2020

Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
G Oh, JE Lee, JC Ye
arXiv preprint arXiv:2011.06337

Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution
E Cha, H Chung, EY Kim, JC Ye
IEEE Transactions on Medical Imaging

Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN
G Oh, B Sim, HJ Chung, L Sunwoo, JC Ye
IEEE Transactions on Computational Imaging 6, 1285-1296

AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising
J Gu, JC Ye
arXiv preprint arXiv:2008.05753

Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data
H Chung, E Cha, L Sunwoo, JC Ye
arXiv preprint arXiv:2008.01362 2020

Cyclegan with a blur kernel for deconvolution microscopy: Optimal transport geometry
S Lim, H Park, SE Lee, S Chang, B Sim, JC Ye
IEEE Transactions on Computational Imaging 6, 1127-1138

Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
J Lee, J Gu, JC Ye
arXiv preprint arXiv:2007.03480

Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks
D Lee, WJ Moon, JC Ye
Nature Machine Intelligence 2 (1), 34-42

Optimal transport, cyclegan, and penalized ls for unsupervised learning in inverse problems
B Sim, G Oh, S Lim, JC Ye
arXiv preprint arXiv:1909.12116

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Music Reference: Damiano Baldoni - Thinking of You