6 citations · 8 across the 3 of their papers we have counts for
5 papers
CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN
Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn +2
Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique which provides spatial distribution of magnetic susceptibility values of tissues. Q…
Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and reduced computational…
Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN
Gyutaek Oh, Byeongsu Sim, Hyungjin Chung +2
Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced runtime compl…
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Eunju Cha, Gyutaek Oh, Jong Chul Ye
Recently, deep learning approaches have been extensively investigated to reconstruct images from accelerated magnetic resonance image (MRI) acquisition. Although these approaches p…
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems
Byeongsu Sim, Gyutaek Oh, Jeongsol Kim +2
To improve the performance of classical generative adversarial network (GAN), Wasserstein generative adversarial networks (W-GAN) was developed as a Kantorovich dual formulation of…