Sungwon Ham

618 total citations
18 papers, 385 citations indexed

About

Sungwon Ham is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Sungwon Ham has authored 18 papers receiving a total of 385 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Pulmonary and Respiratory Medicine and 4 papers in Artificial Intelligence. Recurrent topics in Sungwon Ham's work include Radiomics and Machine Learning in Medical Imaging (7 papers), MRI in cancer diagnosis (4 papers) and AI in cancer detection (3 papers). Sungwon Ham is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), MRI in cancer diagnosis (4 papers) and AI in cancer detection (3 papers). Sungwon Ham collaborates with scholars based in South Korea, Japan and Ethiopia. Sungwon Ham's co-authors include Namkug Kim, Hyunna Lee, Jihye Yun, Ho Sung Kim, Ji Eun Park, Eun‐Jae Lee, Sun U. Kwon, Jong S. Kim, Ji Sung Lee and Han-Bin Lee and has published in prestigious journals such as Stroke, Scientific Reports and Breast Cancer Research.

In The Last Decade

Sungwon Ham

14 papers receiving 379 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sungwon Ham South Korea 9 202 107 90 69 63 18 385
Tal Zeevi United States 13 276 1.4× 98 0.9× 87 1.0× 52 0.8× 75 1.2× 47 441
Beomseok Sohn South Korea 11 257 1.3× 62 0.6× 136 1.5× 30 0.4× 79 1.3× 39 499
Thomas Weißmann Germany 13 210 1.0× 43 0.4× 143 1.6× 49 0.7× 42 0.7× 41 468
Zhenyu Shu China 18 550 2.7× 66 0.6× 117 1.3× 87 1.3× 156 2.5× 50 692
Frederic Madesta Germany 10 368 1.8× 64 0.6× 114 1.3× 169 2.4× 140 2.2× 19 563
Guangying Ruan China 10 149 0.7× 47 0.4× 47 0.5× 29 0.4× 44 0.7× 35 268
Sarv Priya United States 15 308 1.5× 141 1.3× 186 2.1× 28 0.4× 103 1.6× 76 599
Weidao Chen China 11 153 0.8× 40 0.4× 56 0.6× 39 0.6× 72 1.1× 25 254
Khashayar Namdar Canada 10 280 1.4× 27 0.3× 109 1.2× 81 1.2× 71 1.1× 26 470
Zijian Zhou United States 8 124 0.6× 29 0.3× 94 1.0× 22 0.3× 51 0.8× 16 286

Countries citing papers authored by Sungwon Ham

Since Specialization
Citations

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

Fields of papers citing papers by Sungwon Ham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungwon Ham

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

All Works

18 of 18 papers shown
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Lee, Jeea, Sungwon Ham, Namkug Kim, & Hyung Seok Park. (2025). Development of a deep learning-based model for guiding a dissection during robotic breast surgery. Breast Cancer Research. 27(1). 34–34. 1 indexed citations
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Jang, Ryoungwoo, et al.. (2024). Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals. Korean Journal of Radiology. 25(3). 224–224. 22 indexed citations
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Ham, Sungwon, Bo‐Kyung Je, Young‐Jun Rhie, et al.. (2024). Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model. European Radiology. 34(10). 6396–6406. 1 indexed citations
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Ham, Sungwon, Euddeum Shim, Baek Hyun Kim, et al.. (2024). Electron density dual-energy CT can improve the detection of lumbar disc herniation with higher image quality than standard and virtual non-calcium images. European Radiology. 34(11). 7334–7346. 3 indexed citations
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Ham, Sungwon, Jihye Yun, Yun Jung Bae, et al.. (2023). Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA. Scientific Reports. 13(1). 12018–12018. 12 indexed citations
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Jha, Nayansi, Taehun Kim, Sungwon Ham, et al.. (2022). Fully automated condyle segmentation using 3D convolutional neural networks. Scientific Reports. 12(1). 20590–20590. 6 indexed citations
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Lee, Jung Su, Jihye Yun, Sungwon Ham, et al.. (2021). Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis. Scientific Reports. 11(1). 3672–3672. 9 indexed citations
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Suh, Chong Hyun, Kyung Hwa Lee, Young Jun Choi, et al.. (2020). Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status. Scientific Reports. 10(1). 17525–17525. 44 indexed citations
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Park, Ji Eun, Sungwon Ham, Ho Sung Kim, et al.. (2020). Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma. European Radiology. 31(5). 3127–3137. 30 indexed citations
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Kim, Taehun, Sungwon Ham, Beomhee Park, et al.. (2020). Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT. Scientific Reports. 10(1). 366–366. 39 indexed citations
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Lee, Hyunna, Eun‐Jae Lee, Sungwon Ham, et al.. (2020). Machine Learning Approach to Identify Stroke Within 4.5 Hours. Stroke. 51(3). 860–866. 130 indexed citations
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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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