Hyungjin Chung
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Artificial Intelligence
- Computational Mechanics
- Co-authors
- Jong Chul YeEun Sun LeeMichael T. McCannDohoon RyuBurhaneddin YamanMarc KlaskyMehmet AkçakayaLeonard Sunwoo
- Topics
- Medical Imaging Techniques and Applications (5 papers)Advanced Neuroimaging Techniques and Applications (4 papers)Image and Signal Denoising Methods (3 papers)
- Cited by
- Structural BiologyRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Partner nations
- South KoreaUnited StatesHong Kong
In The Last Decade
Hyungjin Chung
17 papers receiving 586 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Radiology, Nuclear Medicine and Imaging 327
- Computer Vision and Pattern Recognition 198
- Biomedical Engineering 126
- Artificial Intelligence 75
- Computational Mechanics 52
Countries citing papers authored by Hyungjin Chung
This map shows the geographic impact of Hyungjin Chung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hyungjin Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyungjin Chung more than expected).
Fields of papers citing papers by Hyungjin Chung
This network shows the impact of papers produced by Hyungjin Chung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hyungjin Chung. The network helps show where Hyungjin Chung may publish in the future.
Co-authorship network of co-authors of Hyungjin Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Hyungjin Chung. A scholar is included among the top collaborators of Hyungjin Chung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hyungjin Chung. Hyungjin Chung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 53 | |
| 4 | 33 | |
| 5 | 1 | |
| 6 | 22 | |
| 7 | 9 | |
| 8 | 44 | |
| 9 | Score-based diffusion models for accelerated MRIbreakdown → | 219 |
| 10 | 0 | |
| 11 | 71 | |
| 12 | 39 | |
| 13 | 9 | |
| 14 | 21 | |
| 15 | Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography. | 1 |
| 16 | 10 | |
| 17 | 8 | |
| 18 | 50 | |
| 19 | 5 |
About Hyungjin Chung
Hyungjin Chung is a scholar working on Structural Biology, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 19 papers that have together received 598 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (5 papers), Advanced Neuroimaging Techniques and Applications (4 papers) and Image and Signal Denoising Methods (3 papers). The work is most often cited by research in Structural Biology (30 citations), Radiology, Nuclear Medicine and Imaging (327 citations) and Computer Vision and Pattern Recognition (198 citations). Hyungjin Chung has collaborated with scholars based in South Korea, United States and Hong Kong. Frequent co-authors include Jong Chul Ye, Eun Sun Lee, Michael T. McCann, Dohoon Ryu, Burhaneddin Yaman, Marc Klasky, Mehmet Akçakaya, Leonard Sunwoo, Sehui Kim and Eunju Cha. Their work appears in journals such as ACS Nano, IEEE Transactions on Medical Imaging and IEEE Signal Processing Magazine.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.