Yaeji Lim
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
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- Thyroid Cancer Diagnosis and Treatment
Papers in
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- Advanced Clustering Algorithms Research 6
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- MRI in cancer diagnosis 9
- Radiomics and Machine Learning in Medical Imaging 6
- Co-authors
- Eun Sook Ko (8 shared papers)Boo‐Kyung Han (7 shared papers)Eun Young Ko (5 shared papers)Soo Yeon Hahn (9 shared papers)Ji Soo Choi (4 shared papers)Jeong Eon Lee (4 shared papers)Seok Jin Nam (2 shared papers)Jae‐Hun Kim (3 shared papers)
- Journals
- Medicine (5 papers)Scientific Reports (4 papers)International Journal of Climatology (3 papers)PLoS ONE (3 papers)Journal of Classification (2 papers)
- Partner nations
- South KoreaUnited StatesEthiopia
In The Last Decade
Yaeji Lim
66 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 120
- Radiology, Nuclear Medicine and Imaging 339
- Endocrinology, Diabetes and Metabolism 106
- Cancer Research 60
- Epidemiology 132
- Physiology 19
Countries citing papers authored by Yaeji Lim
This map shows the geographic impact of Yaeji Lim'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 Yaeji Lim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yaeji Lim more than expected).
Fields of papers citing papers by Yaeji Lim
This network shows the impact of papers produced by Yaeji Lim. 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 Yaeji Lim. The network helps show where Yaeji Lim may publish in the future.
Co-authors
The 25 scholars most cited alongside Yaeji Lim, 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 73 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 191 | |
| 2 | 2016 | 186 | |
| 3 | 2011 | 152 | |
| 4 | 2016 | 37 | |
| 5 | 2016 | 33 | |
| 6 | 2017 | 30 | |
| 7 | 2017 | 30 | |
| 8 | 2015 | 26 | |
| 9 | 2021 | 23 | |
| 10 | 2016 | 22 | |
| 11 | 2019 | 21 | |
| 12 | 2018 | 19 | |
| 13 | 2018 | 19 | |
| 14 | 2020 | 18 | |
| 15 | 1998 | 18 | |
| 16 | 2022 | 18 | |
| 17 | 2016 | 17 | |
| 18 | 2019 | 16 | |
| 19 | 2019 | 13 | |
| 20 | 2016 | 12 |
About Yaeji Lim
Yaeji Lim is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing, Statistics and Probability and Endocrinology, Diabetes and Metabolism, having authored 73 papers that have together received 1.0k indexed citations. Recurring topics across this work include MRI in cancer diagnosis (9 papers), Climate variability and models (7 papers), Thyroid Cancer Diagnosis and Treatment (7 papers), Advanced Statistical Methods and Models (7 papers), Spectroscopy and Chemometric Analyses (6 papers), Advanced Clustering Algorithms Research (6 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Blind Source Separation Techniques (5 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (339 citations), Endocrinology, Diabetes and Metabolism (106 citations), Cancer Research (60 citations), Epidemiology (132 citations) and Physiology (19 citations). Yaeji Lim has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Eun Sook Ko, Boo‐Kyung Han, Eun Young Ko, Soo Yeon Hahn, Ji Soo Choi, Jeong Eon Lee, Seok Jin Nam, Jae‐Hun Kim, Ko Woon Park and Hyunjin Park. Their work appears in journals such as Medicine, Scientific Reports, International Journal of Climatology, PLoS ONE and Journal of Classification.
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.