Kyu-Hwan Jung

1.8k total citations
38 papers, 1.1k citations indexed

About

Kyu-Hwan Jung is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kyu-Hwan Jung has authored 38 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kyu-Hwan Jung's work include Radiomics and Machine Learning in Medical Imaging (8 papers), Lung Cancer Diagnosis and Treatment (7 papers) and COVID-19 diagnosis using AI (7 papers). Kyu-Hwan Jung is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), Lung Cancer Diagnosis and Treatment (7 papers) and COVID-19 diagnosis using AI (7 papers). Kyu-Hwan Jung collaborates with scholars based in South Korea, United States and Germany. Kyu-Hwan Jung's co-authors include Jaemin Son, Sang Jun Park, Jae‐Young Kim, In‐Seok Song, Joon Beom Seo, Kyu Hyung Park, Joo Young Shin, Jaewook Lee, Sang Min Lee and Sohee Park and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Neurology.

In The Last Decade

Kyu-Hwan Jung

37 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kyu-Hwan Jung South Korea 18 679 264 232 226 215 38 1.1k
Jixiang Guo China 17 534 0.8× 235 0.9× 108 0.5× 258 1.1× 242 1.1× 72 999
Gabriella Moraes United Kingdom 12 883 1.3× 481 1.8× 375 1.6× 119 0.5× 117 0.5× 22 1.6k
Joseph R. Ledsam United Kingdom 8 725 1.1× 491 1.9× 85 0.4× 103 0.5× 140 0.7× 14 1.9k
Christoph Kern Germany 14 983 1.4× 449 1.7× 539 2.3× 97 0.4× 120 0.6× 36 1.8k
Yuchen Xie China 12 497 0.7× 79 0.3× 358 1.5× 109 0.5× 21 0.1× 26 748
Eugene Vorontsov Canada 10 742 1.1× 353 1.3× 13 0.1× 195 0.9× 162 0.8× 14 1.2k
Gabriel Chartrand Canada 14 823 1.2× 379 1.4× 12 0.1× 268 1.2× 193 0.9× 19 1.6k
Avinash V. Varadarajan United States 8 1.1k 1.6× 298 1.1× 732 3.2× 181 0.8× 88 0.4× 10 1.6k
Edward Korot United Kingdom 14 760 1.1× 219 0.8× 550 2.4× 112 0.5× 40 0.2× 38 1.2k
Mohith Shamdas United Kingdom 6 478 0.7× 368 1.4× 82 0.4× 61 0.3× 93 0.4× 10 1.1k

Countries citing papers authored by Kyu-Hwan Jung

Since Specialization
Citations

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

Fields of papers citing papers by Kyu-Hwan Jung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyu-Hwan Jung. 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 Kyu-Hwan Jung. The network helps show where Kyu-Hwan Jung may publish in the future.

Co-authorship network of co-authors of Kyu-Hwan Jung

This figure shows the co-authorship network connecting the top 25 collaborators of Kyu-Hwan Jung. A scholar is included among the top collaborators of Kyu-Hwan Jung 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 Kyu-Hwan Jung. Kyu-Hwan Jung 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.
Jung, Kyu-Hwan. (2025). Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations. Healthcare Informatics Research. 31(2). 114–124. 6 indexed citations
2.
3.
Park, Seongjin, et al.. (2024). A large multi-focus dataset for white blood cell classification. Scientific Data. 11(1). 1106–1106. 1 indexed citations
4.
Cho, Seung‐Beom, et al.. (2024). Leveraging Large Language Models for Improved Understanding of Communications With Patients With Cancer in a Call Center Setting: Proof-of-Concept Study. Journal of Medical Internet Research. 26. e63892–e63892. 3 indexed citations
5.
Kim, Pyeong Hwa, Hee Mang Yoon, Jeong Rye Kim, et al.. (2023). Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels. Korean Journal of Radiology. 24(11). 1151–1151. 17 indexed citations
6.
Park, Beomhee, et al.. (2022). Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs. Journal of Digital Imaging. 35(4). 1061–1068. 6 indexed citations
7.
8.
Park, Sohee, Sang Min Lee, Wooil Kim, et al.. (2021). Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning–based CT Section Thickness Reduction. Radiology. 299(1). 211–219. 17 indexed citations
9.
Park, Sohee, Hyunho Park, Sang Min Lee, et al.. (2021). Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement. European Radiology. 32(2). 1054–1064. 18 indexed citations
10.
Jang, Bo Gun, Hyunho Park, Baek‐hui Kim, et al.. (2020). A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies. Clinical Cancer Research. 27(3). 719–728. 31 indexed citations
11.
Park, Sohee, Sang Min Lee, Kyung Hee Lee, et al.. (2019). Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings. European Radiology. 30(3). 1359–1368. 56 indexed citations
12.
Park, Sohee, Sang Min Lee, Kyung‐Hyun Do, et al.. (2019). Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer. Korean Journal of Radiology. 20(10). 1431–1431. 57 indexed citations
13.
Kim, Jae‐Young, et al.. (2019). DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Scientific Reports. 9(1). 17615–17615. 169 indexed citations
14.
Son, Jaemin, Joo Young Shin, Hoon Dong Kim, et al.. (2019). Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images. Ophthalmology. 127(1). 85–94. 145 indexed citations
15.
16.
Park, Se Jin, et al.. (2018). Semi-Supervised Reinforced Active Learning for Pulmonary Nodule Detection in Chest X-rays. 1 indexed citations
17.
Son, Jaemin, Sang Jun Park, & Kyu-Hwan Jung. (2018). Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks. Journal of Digital Imaging. 32(3). 499–512. 146 indexed citations
18.
Kim, Guk Bae, Kyu-Hwan Jung, Yeha Lee, et al.. (2017). Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease. Journal of Digital Imaging. 31(4). 415–424. 81 indexed citations
19.
Kim, Namhyoung, Kyu-Hwan Jung, Yong Seog Kim, & Jae‐Wook Lee. (2012). Uniformly subsampled ensemble (USE) for churn management: Theory and implementation. Expert Systems with Applications. 39(15). 11839–11845. 22 indexed citations
20.
Lee, Daewon, Kyu-Hwan Jung, & Jaewook Lee. (2009). Constructing Sparse Kernel Machines Using Attractors. IEEE Transactions on Neural Networks. 20(4). 721–729. 27 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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