Hyungjoo Cho

1.0k total citations
24 papers, 572 citations indexed

About

Hyungjoo Cho is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Hyungjoo Cho has authored 24 papers receiving a total of 572 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Surgery and 8 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Hyungjoo Cho's work include Digital Holography and Microscopy (8 papers), Cardiac Imaging and Diagnostics (8 papers) and Coronary Interventions and Diagnostics (8 papers). Hyungjoo Cho is often cited by papers focused on Digital Holography and Microscopy (8 papers), Cardiac Imaging and Diagnostics (8 papers) and Coronary Interventions and Diagnostics (8 papers). Hyungjoo Cho collaborates with scholars based in South Korea, United States and Nigeria. Hyungjoo Cho's co-authors include Hyun‐Seok Min, YongKeun Park, YoungJu Jo, Geon Kim, Sang‐Yun Lee, Seung‐Whan Lee, Pil Hyung Lee, Jung‐Min Ahn, Young‐Hak Kim and Cheol Whan Lee and has published in prestigious journals such as Nature Communications, Nature Cell Biology and PLoS Medicine.

In The Last Decade

Hyungjoo Cho

21 papers receiving 559 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyungjoo Cho South Korea 12 223 173 155 154 106 24 572
Hyun‐Seok Min South Korea 15 328 1.5× 238 1.4× 149 1.0× 223 1.4× 98 0.9× 45 809
Yu Xiao China 14 221 1.0× 76 0.4× 150 1.0× 97 0.6× 12 0.1× 38 529
Mariappan S. Nadar United States 14 116 0.5× 27 0.2× 451 2.9× 103 0.7× 29 0.3× 60 736
Christoph Kolbitsch Germany 19 161 0.7× 47 0.3× 921 5.9× 321 2.1× 50 0.5× 91 1.1k
Tahreema Matin United Kingdom 11 142 0.6× 24 0.1× 380 2.5× 151 1.0× 26 0.2× 15 819
Yi-Hwa Liu United States 19 27 0.1× 71 0.4× 715 4.6× 316 2.1× 102 1.0× 67 964
Vy Bui United States 9 176 0.8× 39 0.2× 31 0.2× 56 0.4× 14 0.1× 26 348
Hongwu Ren United States 11 142 0.6× 184 1.1× 222 1.4× 596 3.9× 70 0.7× 20 769
Muthuvel Arigovindan India 10 21 0.1× 90 0.5× 136 0.9× 131 0.9× 30 0.3× 26 427
Bilal Malik United States 18 59 0.3× 265 1.5× 332 2.1× 419 2.7× 48 0.5× 43 816

Countries citing papers authored by Hyungjoo Cho

Since Specialization
Citations

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

Fields of papers citing papers by Hyungjoo Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hyungjoo Cho. 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 Hyungjoo Cho. The network helps show where Hyungjoo Cho may publish in the future.

Co-authorship network of co-authors of Hyungjoo Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Hyungjoo Cho. A scholar is included among the top collaborators of Hyungjoo Cho 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 Hyungjoo Cho. Hyungjoo Cho 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.
Shin, Su‐Jin, Geon Kim, Hyungjoo Cho, et al.. (2025). Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining. Nature Communications. 16(1). 4781–4781. 5 indexed citations
2.
Kim, Kyuri, Hyungjoo Cho, Sung‐Joon Ye, et al.. (2025). A tumor inpainting framework for MRI using automated masks based on channel-specific conditions across the volume. Biomedical Signal Processing and Control. 105. 107579–107579.
3.
4.
Kim, Hyeonmin, June‐Goo Lee, Geunyoung Lee, et al.. (2024). Deep learning model for intravascular ultrasound image segmentation with temporal consistency. The International Journal of Cardiovascular Imaging. 40(11). 2283–2292. 4 indexed citations
5.
Cho, Sungsoo, Hyungjoo Cho, Hyun‐Seok Min, et al.. (2024). Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery. International Journal of Cardiology. 417. 132543–132543.
6.
Shin, Jung Hyun, Ye-Hyun Kim, Myung Kyu Lee, et al.. (2023). Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease. Investigative and Clinical Urology. 64(3). 255–255. 4 indexed citations
7.
Shin, Tae Young, Hyun Ho Han, Hyun‐Seok Min, et al.. (2023). Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy. Medicina. 59(8). 1402–1402. 2 indexed citations
8.
Kim, Ye-Hyun, Yeji Kim, Hyun‐Seok Min, et al.. (2023). Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods. Diagnostics. 13(17). 2849–2849. 9 indexed citations
9.
Jo, YoungJu, Hyungjoo Cho, Wei Sun Park, et al.. (2022). Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning. Conference on Lasers and Electro-Optics. 8. ATh2I.6–ATh2I.6. 2 indexed citations
10.
Min, Hyun‐Seok, Soo-Jin Kang, June‐Goo Lee, et al.. (2021). Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound–Based Deep Learning. JACC: Cardiovascular Interventions. 14(9). 1021–1029. 37 indexed citations
11.
Choi, Jinho, Junwoo Park, Hyungjoo Cho, et al.. (2021). 3D cell instance segmentation via point proposals using cellular components. 1 indexed citations
12.
Cho, Hyungjoo, Soo‐Jin Kang, Hyun‐Seok Min, et al.. (2021). Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease. Atherosclerosis. 324. 69–75. 35 indexed citations
13.
Jo, YoungJu, Hyungjoo Cho, Wei Sun Park, et al.. (2021). Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning. Nature Cell Biology. 23(12). 1329–1337. 78 indexed citations
14.
Cho, Hyungjoo, et al.. (2020). Compton Background Elimination for in Vivo X-Ray Fluorescence Imaging of Gold Nanoparticles Using Convolutional Neural Network. IEEE Transactions on Nuclear Science. 67(11). 2311–2320. 7 indexed citations
15.
Shin, Tae Young, Hyunsuk Kim, Hyun‐Seok Min, et al.. (2020). Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver. Investigative and Clinical Urology. 61(6). 555–555. 19 indexed citations
16.
Min, Hyun‐Seok, Soo-Jin Kang, June‐Goo Lee, et al.. (2020). Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning. EuroIntervention. 16(5). 404–412. 20 indexed citations
17.
Bae, Youngoh, Soo‐Jin Kang, June‐Goo Lee, et al.. (2019). Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning. Atherosclerosis. 288. 168–174. 20 indexed citations
18.
Kang, Soo‐Jin, Won‐Jang Kim, So‐Yeon Choi, et al.. (2018). Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. PLoS Medicine. 15(11). e1002693–e1002693. 36 indexed citations
19.
Kim, Geon, YoungJu Jo, Hyungjoo Cho, et al.. (2018). Automated Identification of Bacteria using Three-Dimensional Holographic Imaging and Convolutional Neural Network. 1–2. 7 indexed citations
20.
Jo, YoungJu, Hyungjoo Cho, Sang‐Yun Lee, et al.. (2018). Quantitative Phase Imaging and Artificial Intelligence: A Review. IEEE Journal of Selected Topics in Quantum Electronics. 25(1). 1–14. 141 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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