Jang‐Hwan Choi

902 total citations
65 papers, 533 citations indexed

About

Jang‐Hwan Choi is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jang‐Hwan Choi has authored 65 papers receiving a total of 533 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Radiology, Nuclear Medicine and Imaging, 37 papers in Biomedical Engineering and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jang‐Hwan Choi's work include Advanced X-ray and CT Imaging (30 papers), Medical Imaging Techniques and Applications (26 papers) and Radiation Dose and Imaging (19 papers). Jang‐Hwan Choi is often cited by papers focused on Advanced X-ray and CT Imaging (30 papers), Medical Imaging Techniques and Applications (26 papers) and Radiation Dose and Imaging (19 papers). Jang‐Hwan Choi collaborates with scholars based in South Korea, United States and Germany. Jang‐Hwan Choi's co-authors include Rebecca Fahrig, Andreas Maier, Martin Berger, Kerstin Müller, Andreas Keil, Garry E. Gold, Joachim Hornegger, Emily J. McWalter, Saikat Pal and Mathias Unberath and has published in prestigious journals such as PLoS ONE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Jang‐Hwan Choi

62 papers receiving 525 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jang‐Hwan Choi South Korea 12 390 370 90 70 64 65 533
Joshy Cyriac Switzerland 11 496 1.3× 271 0.7× 83 0.9× 131 1.9× 46 0.7× 22 789
David H. Foos United States 16 520 1.3× 446 1.2× 120 1.3× 190 2.7× 64 1.0× 49 803
Gregory J. Michalak United States 17 533 1.4× 529 1.4× 68 0.8× 141 2.0× 82 1.3× 34 771
Valerio Fortunati Netherlands 10 207 0.5× 219 0.6× 61 0.7× 39 0.6× 89 1.4× 15 349
Martin Segeroth Switzerland 5 337 0.9× 180 0.5× 78 0.9× 84 1.2× 45 0.7× 11 516
René F. Verhaart Netherlands 13 244 0.6× 298 0.8× 40 0.4× 40 0.6× 91 1.4× 17 447
Dakai Jin United States 15 370 0.9× 149 0.4× 208 2.3× 138 2.0× 29 0.5× 35 697
Mateusz C. Florkow Netherlands 9 239 0.6× 132 0.4× 56 0.6× 45 0.6× 107 1.7× 12 427
Mark Hastenteufel Germany 7 230 0.6× 159 0.4× 166 1.8× 66 0.9× 36 0.6× 19 505
Andreas Fieselmann Germany 12 380 1.0× 237 0.6× 24 0.3× 144 2.1× 22 0.3× 35 535

Countries citing papers authored by Jang‐Hwan Choi

Since Specialization
Citations

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

Fields of papers citing papers by Jang‐Hwan Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jang‐Hwan Choi. 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 Jang‐Hwan Choi. The network helps show where Jang‐Hwan Choi may publish in the future.

Co-authorship network of co-authors of Jang‐Hwan Choi

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

All Works

20 of 20 papers shown
1.
Thies, Mareike, et al.. (2025). Unsupervised motion artifacts reduction for cone-beam CT via enhanced landmark detection. Expert Systems with Applications. 278. 127258–127258.
2.
Yoo, Hongki, et al.. (2024). A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective. Biomedical Engineering Letters. 14(6). 1153–1173. 5 indexed citations
3.
Choi, Jang‐Hwan, et al.. (2023). An unsupervised two‐step training framework for low‐dose computed tomography denoising. Medical Physics. 51(2). 1127–1144. 5 indexed citations
4.
Gold, Garry E., et al.. (2023). Efficient Deep-Detector Image Quality Assessment Based on Knowledge Distillation. IEEE Transactions on Instrumentation and Measurement. 73. 1–15. 2 indexed citations
5.
Woo, Sang Myung, et al.. (2023). Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo-Shapes for Transformer-Based Medical Tasks. IEEE Journal of Biomedical and Health Informatics. 27(4). 2003–2014. 9 indexed citations
6.
Chun, Jung Won, Young Hwan Koh, Jae Hee Cho, et al.. (2023). Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study. Cancers. 15(13). 3392–3392. 10 indexed citations
7.
Lee, Ji Yeon, et al.. (2022). Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising. IEEE Access. 10. 126580–126592. 4 indexed citations
8.
Chae, Seung‐Hoon, et al.. (2022). MFA-net: Object detection for complex X-ray cargo and baggage security imagery. PLoS ONE. 17(9). e0272961–e0272961. 9 indexed citations
9.
Pal, Saikat, Jang‐Hwan Choi, Scott L. Delp, & Michael Fredericson. (2022). Botulinum neurotoxin type A improves vasti muscle balance, patellar tracking, and pain in patients with chronic patellofemoral pain. Journal of Orthopaedic Research®. 41(5). 962–972. 1 indexed citations
10.
Choi, Jang‐Hwan, et al.. (2022). MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences. 7(3). 296–306. 11 indexed citations
11.
Choi, Jang‐Hwan, et al.. (2022). Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer. Sensors. 22(17). 6594–6594. 12 indexed citations
12.
Kang, Mihyun, et al.. (2022). Wavelet subband-specific learning for low-dose computed tomography denoising. PLoS ONE. 17(9). e0274308–e0274308. 8 indexed citations
13.
Beck, Kyongmin Sarah, et al.. (2022). No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning Science and Technology. 3(4). 45033–45033. 16 indexed citations
14.
Maier, Jennifer, Marlies Nitschke, Jang‐Hwan Choi, et al.. (2021). Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Transactions on Biomedical Engineering. 69(5). 1608–1619. 6 indexed citations
15.
Choi, Jang‐Hwan, et al.. (2021). A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding. Applied Sciences. 11(7). 3095–3095. 3 indexed citations
16.
Baek, Jongduk, et al.. (2021). Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications. 32(6). 3 indexed citations
17.
Maier, Jennifer, Andreas Maier, Bjoern M. Eskofier, Rebecca Fahrig, & Jang‐Hwan Choi. (2021). 3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT. IEEE Access. 9. 71821–71831. 1 indexed citations
18.
Yang, Jae‐Suk, Michael Fredericson, & Jang‐Hwan Choi. (2020). The effect of patellofemoral pain syndrome on patellofemoral joint kinematics under upright weight-bearing conditions. PLoS ONE. 15(9). e0239907–e0239907. 6 indexed citations
19.
Choi, Jang‐Hwan & Sooyeul Lee. (2018). Real-Time Tumor Motion Tracking in 3D Using Planning 4D CT Images during Image-Guided Radiation Therapy. Algorithms. 11(10). 155–155. 2 indexed citations
20.
Choi, Jang‐Hwan, Andreas Maier, Andreas Keil, et al.. (2014). Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment. Medical Physics. 41(6Part1). 61902–61902. 36 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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