no code implementations • 4 Oct 2023 • Gyutaek Oh, Baekgyu Choi, Inkyung Jung, Jong Chul Ye
Single-cell RNA sequencing (scRNA-seq) has made significant strides in unraveling the intricate cellular diversity within complex tissues.
no code implementations • 7 Jun 2023 • Gyutaek Oh, Won-Jin Moon, Jong Chul Ye
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters.
no code implementations • 8 Jan 2023 • Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult.
no code implementations • 7 Dec 2020 • Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, Jong Chul Ye
In contrast to the conventional cycleGAN, our novel cycleGAN has only one generator and one discriminator thanks to the known dipole kernel.
no code implementations • 12 Nov 2020 • Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
Recently, deep learning approaches for MR motion artifact correction have been extensively studied.
no code implementations • 29 Aug 2020 • Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Leonard Sunwoo, Jong Chul Ye
Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced runtime complexity.
no code implementations • 17 Mar 2020 • Eunju Cha, Gyutaek Oh, Jong Chul Ye
Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image.
no code implementations • 25 Sep 2019 • Byeongsu Sim, Gyutaek Oh, Sungjun Lim, and Jong Chul Ye
Specifically, we reveal that a cycleGAN architecture can be derived as a dual formulation of the optimal transport problem, if the PLS with a deep learning penalty is used as a transport cost between the two probability measures from measurements and unknown images.
no code implementations • 25 Sep 2019 • Byeongsu Sim, Gyutaek Oh, Jeongsol Kim, Chanyong Jung, Jong Chul Ye
To improve the performance of classical generative adversarial network (GAN), Wasserstein generative adversarial networks (W-GAN) was developed as a Kantorovich dual formulation of the optimal transport (OT) problem using Wasserstein-1 distance.