Na Lae Eun
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
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- Breast Cancer Treatment Studies
Papers in
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- Breast Cancer Treatment Studies 9
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- Ultrasound Imaging and Elastography 4
- MRI in cancer diagnosis 4
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Eun Ju Son (23 shared papers)Jeong‐Ah Kim (19 shared papers)Ji Hyun Youk (21 shared papers)Hye Mi Gweon (15 shared papers)Daesung Kang (3 shared papers)Jeong Seon Park (1 shared paper)Sang Chul Lee (1 shared paper)Hyun Im Moon (1 shared paper)
- Journals
- Korean Journal of Radiology (5 papers)Cancers (4 papers)Radiology (3 papers)European Radiology (3 papers)Clinical Radiology (2 papers)
- Partner nations
- South KoreaGermanyUnited States
In The Last Decade
Na Lae Eun
32 papers receiving 393 citations
Peers
Comparison fields: 5 of 48
- Radiology, Nuclear Medicine and Imaging 107
- Cancer Research 56
- Endocrinology, Diabetes and Metabolism 57
- Health Informatics 5
- Pathology and Forensic Medicine 44
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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 93 | |
| 2 | 2018 | 58 | |
| 3 | 2018 | 34 | |
| 4 | 2019 | 29 | |
| 5 | 2018 | 18 | |
| 6 | 2016 | 16 | |
| 7 | 2021 | 16 | |
| 8 | 2021 | 14 | |
| 9 | 2018 | 13 | |
| 10 | 2016 | 11 | |
| 11 | 2021 | 9 | |
| 12 | 2021 | 8 | |
| 13 | 2016 | 7 | |
| 14 | 2018 | 7 | |
| 15 | 2022 | 7 | |
| 16 | 2019 | 6 | |
| 17 | 2020 | 6 | |
| 18 | 2021 | 6 | |
| 19 | 2023 | 6 | |
| 20 | 2018 | 5 |
About Na Lae Eun
Na Lae Eun is a scholar working on Cancer Research, Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine, Endocrinology, Diabetes and Metabolism and Surgery, having authored 35 papers that have together received 400 indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (9 papers), Breast Lesions and Carcinomas (5 papers), Thyroid Cancer Diagnosis and Treatment (5 papers), Ultrasound Imaging and Elastography (4 papers), MRI in cancer diagnosis (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Breast Implant and Reconstruction (2 papers) and Cervical and Thoracic Myelopathy (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (107 citations), Cancer Research (56 citations), Endocrinology, Diabetes and Metabolism (57 citations), Health Informatics (5 citations) and Pathology and Forensic Medicine (44 citations). Na Lae Eun has collaborated with scholars based in South Korea, Germany and United States. Frequent co-authors include Eun Ju Son, Jeong‐Ah Kim, Ji Hyun Youk, Hye Mi Gweon, Daesung Kang, Jeong Seon Park, Sang Chul Lee, Hyun Im Moon, Yong Wook Kim and Seo Yeon Yoon. Their work appears in journals such as Korean Journal of Radiology, Cancers, Radiology, European 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.