Se‐woon Choe
Impact in
- Genetics top 2%
- Vascular Anomalies and Treatments
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Radiomics and Machine Learning in Medical Imaging 13
- COVID-19 diagnosis using AI 4
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- Photoacoustic and Ultrasonic Imaging 8
- Ultrasound and Hyperthermia Applications 6
- Optical Coherence Tomography Applications 4
- Co-authors
- Gelan Ayana (21 shared papers)Brian S. Sorg (8 shared papers)S. Paul Oh (7 shared papers)Kokeb Dese (4 shared papers)Young Jae Lee (5 shared papers)Hojong Choi (7 shared papers)Yong Hwan Kim (5 shared papers)Minseok Kim (4 shared papers)
- Journals
- Sensors (5 papers)Cancers (4 papers)Biosensors (3 papers)Applied Sciences (2 papers)American Journal Of Pathology (2 papers)
- Partner nations
- South KoreaUnited StatesEthiopia
In The Last Decade
Se‐woon Choe
48 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 126
- Genetics 321
- Radiology, Nuclear Medicine and Imaging 359
- Health Informatics 19
- Biophysics 63
- Neurology 80
Countries citing papers authored by Se‐woon Choe
This map shows the geographic impact of Se‐woon Choe'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 Se‐woon Choe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Se‐woon Choe more than expected).
Fields of papers citing papers by Se‐woon Choe
This network shows the impact of papers produced by Se‐woon Choe. 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 Se‐woon Choe. The network helps show where Se‐woon Choe may publish in the future.
Co-authors
The 25 scholars most cited alongside Se‐woon Choe, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 233 | |
| 2 | 2010 | 127 | |
| 3 | 2021 | 125 | |
| 4 | 2014 | 99 | |
| 5 | 2022 | 84 | |
| 6 | 2023 | 74 | |
| 7 | 2022 | 55 | |
| 8 | 2013 | 50 | |
| 9 | 2022 | 47 | |
| 10 | 2012 | 39 | |
| 11 | 2020 | 37 | |
| 12 | 2018 | 34 | |
| 13 | 2017 | 32 | |
| 14 | 2022 | 29 | |
| 15 | 2021 | 29 | |
| 16 | 2013 | 28 | |
| 17 | 2022 | 27 | |
| 18 | 2019 | 24 | |
| 19 | 2024 | 22 | |
| 20 | 2019 | 20 |
About Se‐woon Choe
Se‐woon Choe is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Artificial Intelligence, Genetics and Computer Vision and Pattern Recognition, having authored 59 papers that have together received 1.4k indexed citations. Recurring topics across this work include AI in cancer detection (15 papers), Radiomics and Machine Learning in Medical Imaging (13 papers), Photoacoustic and Ultrasonic Imaging (8 papers), Ultrasound and Hyperthermia Applications (6 papers), Vascular Anomalies and Treatments (6 papers), Optical Coherence Tomography Applications (4 papers), COVID-19 diagnosis using AI (4 papers) and Colorectal Cancer Screening and Detection (4 papers). The work is most often cited by research in Genetics (321 citations), Radiology, Nuclear Medicine and Imaging (359 citations), Health Informatics (19 citations), Biophysics (63 citations) and Neurology (80 citations). Se‐woon Choe has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Gelan Ayana, Brian S. Sorg, S. Paul Oh, Kokeb Dese, Young Jae Lee, Hojong Choi, Yong Hwan Kim, Minseok Kim, Jingjing Sun and Lei Wu. Their work appears in journals such as Sensors, Cancers, Biosensors, Applied Sciences and American Journal Of Pathology.
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.