Jong Chul Ye

17.5k total citations · 8 hit papers
260 papers, 9.0k citations indexed

About

Jong Chul Ye is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jong Chul Ye has authored 260 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 139 papers in Radiology, Nuclear Medicine and Imaging, 89 papers in Biomedical Engineering and 76 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jong Chul Ye's work include Sparse and Compressive Sensing Techniques (67 papers), Medical Imaging Techniques and Applications (58 papers) and Advanced MRI Techniques and Applications (53 papers). Jong Chul Ye is often cited by papers focused on Sparse and Compressive Sensing Techniques (67 papers), Medical Imaging Techniques and Applications (58 papers) and Advanced MRI Techniques and Applications (53 papers). Jong Chul Ye collaborates with scholars based in South Korea, United States and Switzerland. Jong Chul Ye's co-authors include Yoseob Han, Sungho Tak, Hong Jung, Kyong Hwan Jin, Hyungjin Chung, Eung Yeop Kim, Jaejun Yoo, Bruno De Man, Ge Wang and Eun‐Hee Kang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Jong Chul Ye

244 papers receiving 8.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI

2008 494 citations Jong Chul Ye et al. profile →
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

2018 398 citations Yoseob Han, Jong Chul Ye IEEE Transactions on Medical Imaging profile →
Deep learning for tomographic image reconstruction

2020 357 citations Jong Chul Ye et al. Nature Machine Intelligence profile →
Statistical analysis of fNIRS data: A comprehensive review

2013 340 citations Sungho Tak, Jong Chul Ye profile →
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

2022 259 citations Jong Chul Ye et al. profile →
Score-based diffusion models for accelerated MRI

2022 219 citations Hyungjin Chung, Jong Chul Ye Medical Image Analysis profile →
${k}$ -Space Deep Learning for Accelerated MRI

2019 219 citations Yoseob Han, Jong Chul Ye et al. IEEE Transactions on Medical Imaging profile →
CycleMorph: Cycle consistent unsupervised deformable image registration

2021 175 citations Boah Kim, Jong Chul Ye et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jong Chul Ye South Korea	48	4.7k	3.1k	2.3k	1.4k	863	260	9.0k
Jeffrey A. Fessler United States	61	12.2k 2.6×	6.5k 2.1×	2.0k 0.9×	2.1k 1.5×	986 1.1×	507	16.8k
Charles A. Bouman United States	39	4.0k 0.8×	3.5k 1.1×	3.4k 1.5×	920 0.6×	602 0.7×	349	9.5k
Gábor T. Herman United States	56	6.0k 1.3×	3.7k 1.2×	3.8k 1.6×	2.2k 1.6×	558 0.6×	310	14.1k
Michael Lustig United States	41	9.5k 2.0×	3.1k 1.0×	1.7k 0.8×	4.7k 3.3×	2.4k 2.8×	133	14.2k
Harrison H. Barrett United States	50	6.8k 1.4×	4.5k 1.5×	1.1k 0.5×	421 0.3×	822 1.0×	333	10.2k
Richard M. Leahy United States	63	8.2k 1.7×	3.2k 1.0×	2.6k 1.1×	1.3k 0.9×	607 0.7×	387	20.2k
Dong Liang China	40	3.6k 0.8×	1.9k 0.6×	1.2k 0.5×	1.1k 0.8×	418 0.5×	337	6.2k
David J. Hawkes United Kingdom	49	7.7k 1.6×	3.7k 1.2×	7.5k 3.3×	780 0.6×	191 0.2×	209	15.5k
Allen Tannenbaum United States	55	1.7k 0.3×	1.1k 0.4×	5.7k 2.5×	1.9k 1.3×	170 0.2×	428	15.6k
Jayaram K. Udupa United States	59	3.8k 0.8×	1.8k 0.6×	5.7k 2.5×	803 0.6×	145 0.2×	428	12.1k

Countries citing papers authored by Jong Chul Ye

Since Specialization

Citations

This map shows the geographic impact of Jong Chul Ye'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 Jong Chul Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jong Chul Ye more than expected).

Fields of papers citing papers by Jong Chul Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jong Chul Ye. 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 Jong Chul Ye. The network helps show where Jong Chul Ye may publish in the future.

Co-authorship network of co-authors of Jong Chul Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Jong Chul Ye. A scholar is included among the top collaborators of Jong Chul Ye 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 Jong Chul Ye. Jong Chul Ye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chung, Hyungjin, Tae Hoon Roh, Simon Arridge, et al.. (2025). Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction. IEEE Transactions on Medical Imaging. 44(5). 2093–2104.

Kwon, Gihyun, et al.. (2024). Patch-Wise Graph Contrastive Learning for Image Translation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(12). 13013–13021. 4 indexed citations

Kim, Sehui, et al.. (2024). Fundus Image Enhancement Through Direct Diffusion Bridges. IEEE Journal of Biomedical and Health Informatics. 28(12). 7275–7286. 3 indexed citations

Oh, Ji Eun, et al.. (2024). End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography. Endocrinology and Metabolism. 39(3). 500–510. 4 indexed citations

Ye, Jong Chul, et al.. (2024). Bidirectional generation of structure and properties through a single molecular foundation model. Nature Communications. 15(1). 2323–2323. 29 indexed citations

Park, Sang Joon, Ik Jae Lee, Jun Won Kim, & Jong Chul Ye. (2024). MS-DINO: Masked Self-Supervised Distributed Learning Using Vision Transformer. IEEE Journal of Biomedical and Health Informatics. 28(10). 6180–6192. 2 indexed citations

Park, Sang Joon, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, & Jong Chul Ye. (2023). Self-supervised multi-modal training from uncurated images and reports enables monitoring AI in radiology. Medical Image Analysis. 91. 103021–103021. 14 indexed citations

Wen, Bihan, Saiprasad Ravishankar, Zhizhen Zhao, Raja Giryes, & Jong Chul Ye. (2023). Physics-Driven Machine Learning for Computational Imaging: Part 2 [From the Guest Editors]. IEEE Signal Processing Magazine. 40(2). 13–15. 1 indexed citations

Karl, W.C., James E. Fowler, Charles A. Bouman, et al.. (2023). The Foundations of Computational Imaging: A signal processing perspective. IEEE Signal Processing Magazine. 40(5). 40–53. 5 indexed citations

10.

Park, Sang Joon, et al.. (2023). Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning. Korean Journal of Radiology. 24(6). 541–541. 5 indexed citations

11.

Han, Yoseob, Jaeduck Jang, Eunju Cha, et al.. (2021). Deep learning STEM-EDX tomography of nanocrystals. Nature Machine Intelligence. 3(3). 267–274. 39 indexed citations

12.

Jeong, Jaeheon, et al.. (2020). Unsupervised Denoising for Satellite Imagery Using Wavelet Directional CycleGAN. IEEE Transactions on Geoscience and Remote Sensing. 59(8). 6823–6839. 40 indexed citations

13.

Shin, Yoon Joo, Won Chang, Jong Chul Ye, et al.. (2020). Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm. Korean Journal of Radiology. 21(3). 356–356. 59 indexed citations

14.

Kim, Boah & Jong Chul Ye. (2019). Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning. arXiv (Cornell University). 3 indexed citations

15.

Kang, Eun‐Hee, Junhong Min, & Jong Chul Ye. (2017). Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction. arXiv (Cornell University). 3 indexed citations

16.

Ye, Jong Chul & Yoseob Han. (2017). Deep Convolutional Framelets: A General Deep Learning for Inverse Problems.. arXiv (Cornell University). 3 indexed citations

17.

Kang, Eun‐Hee, et al.. (2017). Wavelet Residual Network for Low-Dose CT via Deep Convolutional Framelets.. arXiv (Cornell University). 6 indexed citations

18.

Ye, Jong Chul, et al.. (2017). Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction. arXiv (Cornell University). 5 indexed citations

19.

Ye, Jong Chul, et al.. (2017). Geometric GAN. arXiv (Cornell University). 44 indexed citations

20.

Tak, Sungho, et al.. (2009). Wavelet minimum description length detrending for near-infrared spectroscopy. Journal of Biomedical Optics. 14(3). 34004–34004. 235 indexed citations

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.

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