Kyong Joon Lee

953 total citations
40 papers, 575 citations indexed

About

Kyong Joon Lee is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kyong Joon Lee has authored 40 papers receiving a total of 575 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Pulmonary and Respiratory Medicine and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kyong Joon Lee's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced Vision and Imaging (6 papers) and Hepatocellular Carcinoma Treatment and Prognosis (5 papers). Kyong Joon Lee is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced Vision and Imaging (6 papers) and Hepatocellular Carcinoma Treatment and Prognosis (5 papers). Kyong Joon Lee collaborates with scholars based in South Korea, Ethiopia and China. Kyong Joon Lee's co-authors include Dongjun Choi, Leonard Sunwoo, Tackeun Kim, Youngjune Kim, Si‐Hyuck Kang, Sang Jun Park, Jae Hyoung Kim, Byung Se Choi, Roh‐Eul Yoo and Jungheum Cho and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Kyong Joon Lee

38 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kyong Joon Lee South Korea 14 217 128 97 88 80 40 575
Tsung‐Ying Ho Taiwan 12 303 1.4× 161 1.3× 63 0.6× 137 1.6× 60 0.8× 24 592
Fayu Liu China 14 143 0.7× 337 2.6× 84 0.9× 45 0.5× 117 1.5× 34 671
Xiangfei Chai China 13 269 1.2× 91 0.7× 164 1.7× 120 1.4× 95 1.2× 19 542
Hwiyoung Kim South Korea 18 466 2.1× 67 0.5× 143 1.5× 215 2.4× 98 1.2× 42 902
P. Meyer France 16 394 1.8× 286 2.2× 229 2.4× 135 1.5× 116 1.4× 65 965
C Martinenghi Italy 13 241 1.1× 137 1.1× 99 1.0× 69 0.8× 55 0.7× 33 499
Barbaros S. Erdal United States 13 281 1.3× 74 0.6× 188 1.9× 113 1.3× 127 1.6× 30 603
Nicolai Oetter Germany 11 131 0.6× 152 1.2× 86 0.9× 48 0.5× 135 1.7× 23 483
Aydın Demircioğlu Germany 16 545 2.5× 113 0.9× 186 1.9× 168 1.9× 132 1.6× 53 861
Masahiro Yakami Japan 14 469 2.2× 97 0.8× 239 2.5× 153 1.7× 197 2.5× 32 756

Countries citing papers authored by Kyong Joon Lee

Since Specialization
Citations

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

Fields of papers citing papers by Kyong Joon Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyong Joon Lee. 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 Kyong Joon Lee. The network helps show where Kyong Joon Lee may publish in the future.

Co-authorship network of co-authors of Kyong Joon Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Kyong Joon Lee. A scholar is included among the top collaborators of Kyong Joon Lee 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 Kyong Joon Lee. Kyong Joon Lee 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.
Chang, Won, et al.. (2025). Accelerating Diffusion: Task-Optimized latent diffusion models for rapid CT denoising. Computers in Biology and Medicine. 194. 110517–110517.
2.
Choi, Dongjun, Leonard Sunwoo, Sung Hyun Baik, et al.. (2024). Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study. American Journal of Neuroradiology. 45(9). 1253–1259. 1 indexed citations
3.
Kim, Tackeun, et al.. (2023). Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning. Computerized Medical Imaging and Graphics. 107. 102220–102220. 2 indexed citations
4.
Choi, Dongjun, Leonard Sunwoo, Sung‐Hye You, Kyong Joon Lee, & Inseon Ryoo. (2023). Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs. Scientific Reports. 13(1). 5337–5337. 2 indexed citations
5.
Jang, Mijung, et al.. (2022). Mask Branch Network: Weakly Supervised Branch Network with a Template Mask for Classifying Masses in 3D Automated Breast Ultrasound. Applied Sciences. 12(13). 6332–6332. 2 indexed citations
6.
Kim, Bo Ram, Yusuhn Kang, Dongjun Choi, et al.. (2022). Tumor grading of soft tissue sarcomas: Assessment with whole-tumor histogram analysis of apparent diffusion coefficient. European Journal of Radiology. 151. 110319–110319. 6 indexed citations
7.
Kang, Yusuhn, Dongjun Choi, Kyong Joon Lee, et al.. (2021). Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning. European Radiology. 31(12). 9408–9417. 14 indexed citations
8.
Yoon, Sung Hyun, Jihang Kim, Kyong Joon Lee, et al.. (2021). Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method. Quantitative Imaging in Medicine and Surgery. 12(3). 1674–1683. 1 indexed citations
9.
Sunwoo, Leonard, et al.. (2021). Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation. Applied Sciences. 11(5). 2014–2014. 23 indexed citations
10.
Shin, Seung Yeon, et al.. (2020). Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval. Computer Vision and Image Understanding. 193. 102901–102901. 7 indexed citations
11.
Cho, Jungheum, Jihang Kim, Jihang Kim, et al.. (2020). Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers. Journal of Clinical Medicine. 9(12). 3908–3908. 6 indexed citations
12.
Lee, Kyong Joon, Inseon Ryoo, Dongjun Choi, et al.. (2020). Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid. PLoS ONE. 15(11). e0241796–e0241796. 11 indexed citations
13.
Byun, Seong Jun, Soochahn Lee, Tackeun Kim, et al.. (2020). Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Scientific Reports. 10(1). 4623–4623. 47 indexed citations
14.
Kim, Youngjune, Kyong Joon Lee, Leonard Sunwoo, et al.. (2018). Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography. Investigative Radiology. 54(1). 7–15. 87 indexed citations
15.
Ik, Dong, Min Woo Lee, Tae Wook Kang, et al.. (2017). Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods. Acta Radiologica. 58(11). 1349–1357. 13 indexed citations
16.
Ik, Dong, Min Woo Lee, Kyoung Doo Song, et al.. (2017). A prospective comparison between auto-registration and manual registration of real-time ultrasound with MR images for percutaneous ablation or biopsy of hepatic lesions. Abdominal Radiology. 42(6). 1799–1808. 11 indexed citations
17.
Sunwoo, Leonard, Young Jae Kim, Kwang Gi Kim, et al.. (2017). Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study. PLoS ONE. 12(6). e0178265–e0178265. 40 indexed citations
18.
Ko, Yousun, Jihang Kim, Haeryoung Kim, et al.. (2017). Limited detection of small (≤ 10 mm) colorectal liver metastasis at preoperative CT in patients undergoing liver resection. PLoS ONE. 12(12). e0189797–e0189797. 17 indexed citations
19.
Ik, Dong, Min Woo Lee, Tae Wook Kang, et al.. (2017). Comparison Between CT and MR Images as More Favorable Reference Data Sets for Fusion Imaging-Guided Radiofrequency Ablation or Biopsy of Hepatic Lesions: A Prospective Study with Focus on Patient’s Respiration. CardioVascular and Interventional Radiology. 40(10). 1567–1575. 5 indexed citations
20.
Kim, Seona, et al.. (2012). A layered inpainting method for virtual view synthesis. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8288. 82882A–82882A.

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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