Na Lae Eun
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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- Breast Cancer Treatment Studies
Papers in
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- Radiomics and Machine Learning in Medical Imaging 9
- MRI in cancer diagnosis 8
- Ultrasound Imaging and Elastography 4
- Medical Imaging Techniques and Applications 3
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- Breast Lesions and Carcinomas 10
- Co-authors
- Eun Ju Son (21 shared papers)Ji Hyun Youk (19 shared papers)Hye Mi Gweon (15 shared papers)Jeong‐Ah Kim (17 shared papers)Daesung Kang (3 shared papers)Jeong Seon Park (1 shared paper)Seo Yeon Yoon (2 shared papers)Hyun Im Moon (1 shared paper)
- Journals
- Korean Journal of Radiology (5 papers)Cancers (4 papers)European Radiology (3 papers)Radiology (3 papers)Clinical Radiology (2 papers)
- Partner nations
- South KoreaGermanySpain
In The Last Decade
Na Lae Eun
28 papers receiving 342 citations
Peers
Comparison fields: 5 of 53
- Radiology, Nuclear Medicine and Imaging 188
- Cancer Research 81
- Health Informatics 7
- Pathology and Forensic Medicine 90
- Endocrinology, Diabetes and Metabolism 51
Countries citing papers authored by Na Lae Eun
This map shows the geographic impact of Na Lae Eun'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 Na Lae Eun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Na Lae Eun more than expected).
Fields of papers citing papers by Na Lae Eun
This network shows the impact of papers produced by Na Lae Eun. 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 Na Lae Eun. The network helps show where Na Lae Eun may publish in the future.
Co-authors
The 25 scholars most cited alongside Na Lae Eun, 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 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 82 | |
| 2 | 2018 | 51 | |
| 3 | 2018 | 31 | |
| 4 | 2019 | 29 | |
| 5 | 2018 | 16 | |
| 6 | 2016 | 15 | |
| 7 | 2021 | 15 | |
| 8 | 2018 | 13 | |
| 9 | 2021 | 12 | |
| 10 | 2016 | 9 | |
| 11 | 2021 | 8 | |
| 12 | 2018 | 7 | |
| 13 | 2019 | 6 | |
| 14 | 2021 | 6 | |
| 15 | 2021 | 5 | |
| 16 | 2022 | 5 | |
| 17 | 2016 | 5 | |
| 18 | 2018 | 5 | |
| 19 | 2023 | 4 | |
| 20 | 2020 | 4 |
About Na Lae Eun
Na Lae Eun is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine, Cancer Research, Surgery and Oncology, having authored 33 papers that have together received 345 indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (11 papers), Breast Lesions and Carcinomas (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), MRI in cancer diagnosis (8 papers), Thyroid Cancer Diagnosis and Treatment (5 papers), Ultrasound Imaging and Elastography (4 papers), AI in cancer detection (4 papers) and Medical Imaging Techniques and Applications (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (188 citations), Cancer Research (81 citations), Health Informatics (7 citations), Pathology and Forensic Medicine (90 citations) and Endocrinology, Diabetes and Metabolism (51 citations). Na Lae Eun has collaborated with scholars based in South Korea, Germany and Spain. Frequent co-authors include Eun Ju Son, Ji Hyun Youk, Hye Mi Gweon, Jeong‐Ah Kim, Daesung Kang, Jeong Seon Park, Seo Yeon Yoon, Hyun Im Moon, Sang Chul Lee and Yong Wook Kim. Their work appears in journals such as Korean Journal of Radiology, Cancers, European Radiology, Radiology and Clinical Radiology.
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.