Jongseong Jang

607 citations
20 papers · 350 · h-index 9

Impact in

Papers in

Jongseong Jang

20 papers receiving 348 citations

Peers

Jongseong Jang
Comparison fields: 5 of 77
  • Health Informatics 34
  • Computer Vision and Pattern Recognition 119
  • Artificial Intelligence 158
  • Radiology, Nuclear Medicine and Imaging 75
  • Media Technology 17
Replace Miguel A. Molina‐Cabello with:
Miguel A. Molina‐Cabello Spain
Xiaozheng Xie China
Jiho Choi South Korea
Abdelrahman Shaker United Arab Emirates
Xiao-Yun Zhou China
Gangming Zhao China
Hossein Kashiani United States
May Phu Paing Thailand
Chuanbin Liu China
Gi-Tae Han South Korea
Jongseong Jang relative to Miguel A. Molina‐Cabello Spain Miguel A. Molina‐Cabello's profile →
Citations per field
00.5×1.5×
Miguel A. Molina‐Cabello · 1×
Citations per year

Countries citing papers authored by Jongseong Jang

Since Specialization
Citations

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

Fields of papers citing papers by Jongseong Jang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jongseong Jang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jongseong Jang Line = papers co-authored together Jongseong Jang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202199
2 201742
3 202341
4 202038
5 202228
6 201923
7 202121
8 202115
9 20169
10 20198
11 20217
12 20245
13 20145
14 20132
15 20172
16 20151
17 20171
18 20141
19 20131
20 20221

About Jongseong Jang

Jongseong Jang is a scholar working on Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Surgery, having authored 20 papers that have together received 350 indexed citations. Recurring topics across this work include Soft Robotics and Applications (5 papers), Explainable Artificial Intelligence (XAI) (3 papers), Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Multimodal Machine Learning Applications (2 papers), Hip disorders and treatments (2 papers) and Robotic Path Planning Algorithms (2 papers). The work is most often cited by research in Health Informatics (34 citations), Computer Vision and Pattern Recognition (119 citations), Artificial Intelligence (158 citations), Radiology, Nuclear Medicine and Imaging (75 citations) and Media Technology (17 citations). Jongseong Jang has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Zheda Mai, Scott Sanner, Young Soo Kim, Jihwan Jeong, Hyunwoo Kim, Yee-Suk Kim, Chang Lee, Chulhong Kim, Zhibo Zhang and Jeesu Kim. Their work appears in journals such as Scientific Reports, IEEE Transactions on Medical Imaging, American Journal of Roentgenology, IEEE Transactions on Industrial Informatics and Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine.

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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