Jihye Yun
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 10%
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Namkug KimYongwon ChoJoon Beom SeoHyun‐Jin BaeKeewon ShinRyoungwoo JangMingyu KimSungwon Ham
- Topics
- Radiomics and Machine Learning in Medical Imaging (19 papers)Lung Cancer Diagnosis and Treatment (18 papers)COVID-19 diagnosis using AI (8 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Partner nations
- South KoreaUnited StatesBelarus
In The Last Decade
Jihye Yun
31 papers receiving 973 citations
Peers
Comparison fields: 5 of 111
- Radiology, Nuclear Medicine and Imaging 564
- Pulmonary and Respiratory Medicine 323
- Artificial Intelligence 190
- Biomedical Engineering 137
- Computer Vision and Pattern Recognition 110
Countries citing papers authored by Jihye Yun
This map shows the geographic impact of Jihye Yun'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 Jihye Yun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jihye Yun more than expected).
Fields of papers citing papers by Jihye Yun
This network shows the impact of papers produced by Jihye Yun. 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 Jihye Yun. The network helps show where Jihye Yun may publish in the future.
Co-authorship network of co-authors of Jihye Yun
This figure shows the co-authorship network connecting the top 25 collaborators of Jihye Yun. A scholar is included among the top collaborators of Jihye Yun 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 Jihye Yun. Jihye Yun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 17 | |
| 6 | 4 | |
| 7 | 6 | |
| 8 | 23 | |
| 9 | 5 | |
| 10 | 25 | |
| 11 | 83 | |
| 12 | 9 | |
| 13 | 23 | |
| 14 | 11 | |
| 15 | 32 | |
| 16 | 44 | |
| 17 | 0 | |
| 18 | 77 | |
| 19 | 82 | |
| 20 | 75 |
About Jihye Yun
Jihye Yun is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Pulmonary and Respiratory Medicine, having authored 36 papers that have together received 985 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (19 papers), Lung Cancer Diagnosis and Treatment (18 papers) and COVID-19 diagnosis using AI (8 papers). The work is most often cited by research in Health Informatics (56 citations), Radiology, Nuclear Medicine and Imaging (564 citations) and Pulmonary and Respiratory Medicine (323 citations). Jihye Yun has collaborated with scholars based in South Korea, United States and Belarus. Frequent co-authors include Namkug Kim, Yongwon Cho, Joon Beom Seo, Hyun‐Jin Bae, Keewon Shin, Ryoungwoo Jang, Mingyu Kim, Sungwon Ham, Sang Min Lee and Ho Sung Kim. Their work appears in journals such as Scientific Reports, Radiology and Physics in Medicine and Biology.
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.