Joonsang Lee

1.1k total citations
23 papers, 470 citations indexed

About

Joonsang Lee is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Joonsang Lee has authored 23 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Genetics and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Joonsang Lee's work include Radiomics and Machine Learning in Medical Imaging (13 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (6 papers). Joonsang Lee is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (13 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (6 papers). Joonsang Lee collaborates with scholars based in United States, South Korea and India. Joonsang Lee's co-authors include Arvind Rao, Laurence E. Court, Jinzhong Yang, Dennis Mackin, Ganesh Rao, Dalu Yang, Michael Lehrer, Xenia Fave, Lifei Zhang and Constance A. Owens and has published in prestigious journals such as Scientific Reports, Applied Surface Science and International Journal of Computer Vision.

In The Last Decade

Joonsang Lee

22 papers receiving 465 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joonsang Lee United States 11 355 128 102 72 68 23 470
Ramón Correa United States 11 372 1.0× 198 1.5× 85 0.8× 110 1.5× 93 1.4× 30 526
Tanja Schneider Germany 10 195 0.5× 77 0.6× 53 0.5× 116 1.6× 22 0.3× 25 428
Hatef Mehrabian Canada 14 403 1.1× 112 0.9× 85 0.8× 107 1.5× 12 0.2× 32 572
Frederic Madesta Germany 10 368 1.0× 46 0.4× 140 1.4× 114 1.6× 169 2.5× 19 563
Kanabu Nawa Japan 9 385 1.1× 31 0.2× 137 1.3× 170 2.4× 58 0.9× 23 495
Erica Pollack United States 8 355 1.0× 119 0.9× 47 0.5× 71 1.0× 106 1.6× 20 562
Sascha Zelzer Germany 9 265 0.7× 25 0.2× 133 1.3× 99 1.4× 21 0.3× 12 480
Yong Sub Song South Korea 13 440 1.2× 108 0.8× 38 0.4× 295 4.1× 33 0.5× 19 627
Se Byeong Lee South Korea 19 386 1.1× 72 0.6× 133 1.3× 809 11.2× 39 0.6× 95 1.2k
Bilgin Keserci Vietnam 22 354 1.0× 20 0.2× 372 3.6× 55 0.8× 70 1.0× 44 1.0k

Countries citing papers authored by Joonsang Lee

Since Specialization
Citations

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

Fields of papers citing papers by Joonsang Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joonsang Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Joonsang Lee. A scholar is included among the top collaborators of Joonsang 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 Joonsang Lee. Joonsang 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.
Warner, Elisa, Joonsang Lee, William Hsu, et al.. (2024). Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects. International Journal of Computer Vision. 132(9). 3753–3769. 14 indexed citations
2.
Warner, Elisa, Joonsang Lee, Nicholas Wang, et al.. (2023). Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI. PubMed. 2023. 1–4.
3.
Lee, Joonsang, et al.. (2023). Association of graph-based spatial features with overall survival status of glioblastoma patients. Scientific Reports. 13(1). 17046–17046. 1 indexed citations
4.
Lee, Joonsang, Elisa Warner, Markus Bitzer, et al.. (2023). Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images. Scientific Reports. 13(1). 12701–12701. 5 indexed citations
5.
Lee, Joonsang, Elisa Warner, Markus Bitzer, et al.. (2022). Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease. Scientific Reports. 12(1). 4832–4832. 28 indexed citations
6.
Caissie, Amanda, Michelle Mierzwa, Clifton D. Fuller, et al.. (2022). Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium. Advances in Radiation Oncology. 8(1). 100925–100925. 2 indexed citations
7.
Wang, Nicholas, Jeremy Kaplan, Joonsang Lee, et al.. (2021). Stress Testing Pathology Models with Generated Artifacts. Journal of Pathology Informatics. 12(1). 54–54. 7 indexed citations
8.
Lee, Joonsang, et al.. (2020). Interfacial behavior of surfactant-covered double emulsion in extensional flow. Physical review. E. 102(5). 53104–53104. 1 indexed citations
9.
Lee, Joonsang, Nicholas Wang, Sevcan Türk, et al.. (2020). Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning. Scientific Reports. 10(1). 20331–20331. 49 indexed citations
10.
Kim, Sang-Hoon, et al.. (2019). Effects of Back Pressure on Flow Regime and Suction Performance of Gas–Liquid Swirl Ejector. International Journal of Chemical Reactor Engineering. 17(9). 2 indexed citations
11.
Anderson, Brian, Skylar Gay, Xenia Fave, et al.. (2017). Cost‐effective immobilization for whole brain radiation therapy. Journal of Applied Clinical Medical Physics. 18(4). 116–122. 5 indexed citations
12.
Saha, Abhijoy, Sebastian Kurtek, Joonsang Lee, et al.. (2016). DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. NeuroImage Clinical. 12. 132–143. 12 indexed citations
13.
Lehrer, Michael, et al.. (2016). Radiomics in glioblastoma: current status, challenges and potential opportunities. Translational Cancer Research. 5(4). 383–397. 55 indexed citations
14.
Court, Laurence E., Xenia Fave, Dennis Mackin, et al.. (2016). Computational resources for radiomics. Translational Cancer Research. 5(4). 340–348. 55 indexed citations
15.
Lee, Joonsang, et al.. (2015). Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma. American Journal of Neuroradiology. 37(1). 37–43. 56 indexed citations
16.
17.
Lee, Joonsang, et al.. (2014). Comparison of analytical and numerical analysis of the reference region model for DCE-MRI. Magnetic Resonance Imaging. 32(7). 845–853. 3 indexed citations
18.
Lee, Joonsang, Simon R. Platt, Marc Kent, & Qun Zhao. (2011). An analysis of the pharmacokinetic parameter ratios in DCE-MRI using the reference region model. Magnetic Resonance Imaging. 30(1). 26–35. 8 indexed citations
19.
Abell, Justin, Joonsang Lee, Qun Zhao, Harold Szu, & Yiping Zhao. (2011). Differentiating intrinsic SERS spectra from a mixture by sampling induced composition gradient and independent component analysis. The Analyst. 137(1). 73–76. 8 indexed citations
20.
Lee, Joonsang. (2010). Quantification of DCE-MRI: Pharmacokinetic parameter ratio between TOI and RR in reference region model. PubMed. 2010. 2837–2840. 1 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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