Soopil Kim
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- AI in cancer detection
Papers in
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- Privacy-Preserving Technologies in Data 4
- Domain Adaptation and Few-Shot Learning 4
- Adversarial Robustness in Machine Learning 2
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- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Sang Hyun Park (16 shared papers)Philip Chikontwe (11 shared papers)Kilian M. Pohl (6 shared papers)Ehsan Adeli (6 shared papers)Kyong Hwan Jin (5 shared papers)Seung‐Koo Lee (2 shared papers)Yae Won Park (2 shared papers)Se Hoon Kim (2 shared papers)
- Journals
- Medical Image Analysis (2 papers)Pattern Recognition (2 papers)European Radiology (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)Information Fusion (1 paper)
- Partner nations
- South KoreaUnited States
In The Last Decade
Soopil Kim
13 papers receiving 249 citations
Peers
Comparison fields: 5 of 57
- Health Informatics 6
- Artificial Intelligence 114
- Cognitive Neuroscience 55
- Computer Vision and Pattern Recognition 56
- Radiology, Nuclear Medicine and Imaging 47
Countries citing papers authored by Soopil Kim
This map shows the geographic impact of Soopil 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 Soopil Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soopil Kim more than expected).
Fields of papers citing papers by Soopil Kim
This network shows the impact of papers produced by Soopil 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 Soopil Kim. The network helps show where Soopil Kim may publish in the future.
Co-authors
The 15 scholars most cited alongside Soopil Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 33 | |
| 2 | 2022 | 33 | |
| 3 | 2020 | 31 | |
| 4 | 2020 | 29 | |
| 5 | 2023 | 28 | |
| 6 | 2023 | 20 | |
| 7 | 2024 | 18 | |
| 8 | 2023 | 15 | |
| 9 | 2024 | 11 | |
| 10 | 2020 | 10 | |
| 11 | 2024 | 8 | |
| 12 | 2023 | 7 | |
| 13 | 2022 | 6 | |
| 14 | 2025 | 0 | |
| 15 | 2025 | 0 | |
| 16 | 2025 | 0 |
About Soopil Kim
Soopil Kim is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Signal Processing, having authored 16 papers that have together received 249 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Blind Source Separation Techniques (3 papers), EEG and Brain-Computer Interfaces (3 papers), Adversarial Robustness in Machine Learning (2 papers), Medical Imaging and Analysis (2 papers) and Meningioma and schwannoma management (2 papers). The work is most often cited by research in Health Informatics (6 citations), Artificial Intelligence (114 citations), Cognitive Neuroscience (55 citations), Computer Vision and Pattern Recognition (56 citations) and Radiology, Nuclear Medicine and Imaging (47 citations). Soopil Kim has collaborated with scholars based in South Korea and United States. Frequent co-authors include Sang Hyun Park, Philip Chikontwe, Kilian M. Pohl, Ehsan Adeli, Kyong Hwan Jin, Seung‐Koo Lee, Yae Won Park, Se Hoon Kim, Jong Hee Chang and Sung Soo Ahn. Their work appears in journals such as Medical Image Analysis, Pattern Recognition, European Radiology, IEEE Transactions on Neural Networks and Learning Systems and Information Fusion.
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.