Hyun‐Chul Kim
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Cognitive Neuroscience top 10%
- Co-authors
- Sung Yang BangDaijin KimZoubin GhahramaniHong-Mo JeShaoning PangJong‐Hwan LeeSeung-Schik YooWonhye Lee
- Topics
- Ultrasound and Hyperthermia Applications (10 papers)Face and Expression Recognition (8 papers)Functional Brain Connectivity Studies (8 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Partner nations
- South KoreaUnited StatesGermany
In The Last Decade
Hyun‐Chul Kim
54 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 157
- Biomedical Engineering 423
- Artificial Intelligence 345
- Computer Vision and Pattern Recognition 292
- Radiology, Nuclear Medicine and Imaging 237
- Cognitive Neuroscience 192
Countries citing papers authored by Hyun‐Chul Kim
This map shows the geographic impact of Hyun‐Chul Kim'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 Hyun‐Chul Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyun‐Chul Kim more than expected).
Fields of papers citing papers by Hyun‐Chul Kim
This network shows the impact of papers produced by Hyun‐Chul Kim. 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 Hyun‐Chul Kim. The network helps show where Hyun‐Chul Kim may publish in the future.
Co-authorship network of co-authors of Hyun‐Chul Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Hyun‐Chul Kim. A scholar is included among the top collaborators of Hyun‐Chul Kim 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 Hyun‐Chul Kim. Hyun‐Chul Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 12 | |
| 4 | 7 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 9 | |
| 8 | 3 | |
| 9 | 8 | |
| 10 | 22 | |
| 11 | Deep-Learning-based Plant Anomaly Detection using a Drone | 1 |
| 12 | 3 | |
| 13 | 0 | |
| 14 | 35 | |
| 15 | 23 | |
| 16 | 289 | |
| 17 | 37 | |
| 18 | Bayesian Classifier Combination | 99 |
| 19 | 11 | |
| 20 | 17 |
About Hyun‐Chul Kim
Hyun‐Chul Kim is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 58 papers that have together received 1.5k indexed citations. Recurring topics across this work include Ultrasound and Hyperthermia Applications (10 papers), Face and Expression Recognition (8 papers) and Functional Brain Connectivity Studies (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (292 citations), Artificial Intelligence (345 citations) and Radiology, Nuclear Medicine and Imaging (237 citations). Hyun‐Chul Kim has collaborated with scholars based in South Korea, United States and Germany. Frequent co-authors include Sung Yang Bang, Daijin Kim, Zoubin Ghahramani, Hong-Mo Je, Shaoning Pang, Jong‐Hwan Lee, Seung-Schik Yoo, Wonhye Lee, Yujin Jung and Yong An Chung. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and NeuroImage.
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.