Si Eun Lee

48 papers receiving 1.6k citations

Peers

Si Eun Lee
Comparison fields: 5 of 113
  • Biochemistry 142
  • Epidemiology 722
  • Obstetrics and Gynecology 135
  • Health Informatics 25
  • Microbiology 110
Replace Kangfeng Jiang with:
Kangfeng Jiang China
Asami Yagi Japan
Haichong Wu China
Geraldo Picheth Brazil
Bassem Refaat Saudi Arabia
Linhua Zhao China
Shanyi Li United States
Taiping He China
Octavian Creţu Romania
Dong Shang China
Si Eun Lee relative to Kangfeng Jiang China Kangfeng Jiang's profile →
Citations per field
00.5×7.7×
Kangfeng Jiang · 1×
Citations per year

Countries citing papers authored by Si Eun Lee

Since Specialization
Citations

This map shows the geographic impact of Si Eun Lee'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 Si Eun Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Si Eun Lee more than expected).

Fields of papers citing papers by Si Eun Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Si Eun Lee. 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 Si Eun Lee. The network helps show where Si Eun Lee may publish in the future.

Co-authors

The 25 scholars most cited alongside Si Eun Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Si Eun Lee Line = papers co-authored together Si Eun Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2003361
2 2008164
3 2010125
4 2007111
5 2008110
6 201887
7 200785
8 200669
9 200961
10 200856
11 200951
12 200247
13 202045
14 200942
15 200939
16 200831
17 202227
18 200824
19 201717
20 202217

About Si Eun Lee

Si Eun Lee is a scholar working on Artificial Intelligence, Epidemiology, Pulmonary and Respiratory Medicine, Public Health, Environmental and Occupational Health and Radiology, Nuclear Medicine and Imaging, having authored 53 papers that have together received 1.7k indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Preterm Birth and Chorioamnionitis (11 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Pregnancy-related medical research (4 papers), Thyroid Cancer Diagnosis and Treatment (4 papers), Digital Radiography and Breast Imaging (4 papers), Global Cancer Incidence and Screening (4 papers) and Breast Cancer Treatment Studies (3 papers). The work is most often cited by research in Biochemistry (142 citations), Epidemiology (722 citations), Obstetrics and Gynecology (135 citations), Health Informatics (25 citations) and Microbiology (110 citations). Si Eun Lee has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Roberto Romero, Bo Hyun Yoon, Jeong Hee Kim, Hyun Jin Hwang, Eun‐Kyung Kim, Chan‐Wook Park, Jong Kwan Jun, Joong Shin Park, Hyo Suk Seong and Kyunghwa Han. Their work appears in journals such as European Radiology, The Journal of Maternal-Fetal & Neonatal Medicine, American Journal of Obstetrics and Gynecology, Journal of Perinatal Medicine and ULTRASONOGRAPHY.

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.

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