Blog - Hyungjin

📚 Welcome to my Homepage

Welcome to my personal homepage. I am an ongoing Ph.D. student @KAIST. The motivation of my research is “Making invisible, visible”. Mostly, I work on solving inverse problems arising in computational imaging, and I focus on using deep learning, especially unsupervised learning methods to accomplish it. Unlike many works in literature that simply adopt published methods off-the-shelf, I also try to delve on theoretical links of machine learning and signal processing.

Hyungjin Chung

Ph.D. student - Deep Learning & Inverse Problems

Korea Advanced Institute of Science and Technology (KAIST) BISPL

Biography

Hyungjin Chung is a Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). His research interests include, but is not restricted to solving inverse problems via deep learning. He has published papers and ongoing projects in a wide variety of fields including compressed sensing MRI (CS-MRI), tomographic reconstruction, microscopy, and phase retrieval.

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Interests

Deep Learning
Diffusion Model
Energy-based Model
Computational Imaging
Inverse Problems

Education

MS in Bio & Brain Engineering, 2021
Korea Advanced Institute of Science & Technology (KAIST)
BS in Biomedical Engineering, 2019
Korea University

Projects

Probabilistic Machine Learning - Kevin Murphy

Brief summary, guidance, and solutions to the famous machine-learning bible “Probabilistic Machine Learning” in Korean.

News

Hyungjin Chung

Apr 27, 2021 12:00 AM

Interview at BRIC

Interview at BRIC for the publication of “Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data” in Medical Image Analysis, 2021.

Hyungjin Chung

Mar 21, 2021 12:00 AM

Nature Machine Intelligence Cover selected

The paper I have co-authored, link is featured as the cover of the March issue in Nature Machine Intelligence

Featured Publications

Hyungjin Chung, Jong Chul Ye

October 2021 In ArXiv

Score-based diffusion models for accelerated MRI

Score-based diffusion models beat supervised learning methods on MRI reconstruction.

Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye

February 2021 In Nat. Mach. Int.

Deep learning STEM-EDX tomography of nanocrystals

Resolution of STEM-EDX tomography is greatly enhanced through unsupervised deep learning. We verified the method by comparing the optical properties.

Hyungjin Chung, Jong Chul Ye

May 2020 In NMI

Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN

SliceGAN, stochastic model to synthesize 3D microstructures from 2D images, is endowed with the ability to disentangle features, and continuously control these features via AdaIN.

Hyungjin Chung, Jaehyun Kim, Jeong Hee Yoon, Jeong Min Lee, Jong Chul Ye

May 2020 In ArXiv

Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning

Unsupervised deep learning for simultaneous super-resolution and motion artifact removal of diffusion-weighted MRI scans is proposed.

Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye

April 2020 In Medical Image Analysis

Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data

Two-stage unsupervised reconstruction method for 3D TOF-MRA is developed. A novel projection discriminator in the axial reconstruction step drastically enhances the vessel visiblity.

Joonhyung Lee, Hyunjong Kim, Hyungjin Chung, Jong Chul Ye

March 2020 IEEE ISBI

Deep Learning Fast MRI Using Channel Attention in Magnitude Domain

BarbellNet, which consists of long stack of residual channel attention block(RCAB) was proposed for the reconstruction of fast MRI reconstruction. Reconstruction results through this model was placed 6th in the NeurIPS2020 fastMRI challenge.

Hyungjin Chung, Jaeyoung Huh, Geon Kim, Yong Keun Park, Jong Chul Ye

March 2020 In IEEE TCI

Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain

Unsupervised missing-cone resolving method is proposed in the context of ODT.

Recent Publications

Joon Yeul Nam, Hyungjin Chung, Kyu Sung Choi, Hyuk Lee, Seung Jun Han, Tae Jun Kim, Hosim Soh, Eun Kang, Soo_jeong Cho, Jong Chul Ye, Jong Pil Im, Sang Gyun Kim, Yoon Jun Kim, Joo Sung Kim, Jung-Hwan Yoo, Hyunsoo Chung, Jeong-Hoon Lee (2021). Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison. GIE.

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Hyungjin Chung, Jong Chul Ye (2021). Score-based diffusion models for accelerated MRI. In ArXiv.

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Mehmet Akcakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye (2021). Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement. IEEE SPM.

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Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye (2021). Deep learning STEM-EDX tomography of nanocrystals. In Nat. Mach. Int..

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Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Leonard Sunwoo, Jong Chul Ye (2020). Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN. IEEE TCI.

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Eunju Cha, Hyungjin Chung, Eung Yeop Kim, Jong Chul Ye (2020). Unpaired training of deep learning tMRA for flexible spatio-temporal resolution. IEEE TMI.

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Hyungjin Chung, Jong Chul Ye (2020). Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN. In NMI.

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Hyungjin Chung, Jaehyun Kim, Jeong Hee Yoon, Jeong Min Lee, Jong Chul Ye (2020). Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning. In ArXiv.

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Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye (2020). Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data. In Medical Image Analysis.

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Joonhyung Lee, Hyunjong Kim, Hyungjin Chung, Jong Chul Ye (2020). Deep Learning Fast MRI Using Channel Attention in Magnitude Domain. IEEE ISBI.