Geunyoung Lee

985 total citations
20 papers, 464 citations indexed

About

Geunyoung Lee is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Health Information Management. According to data from OpenAlex, Geunyoung Lee has authored 20 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Ophthalmology and 6 papers in Health Information Management. Recurrent topics in Geunyoung Lee's work include Retinal Imaging and Analysis (14 papers), Retinal and Optic Conditions (6 papers) and Artificial Intelligence in Healthcare (6 papers). Geunyoung Lee is often cited by papers focused on Retinal Imaging and Analysis (14 papers), Retinal and Optic Conditions (6 papers) and Artificial Intelligence in Healthcare (6 papers). Geunyoung Lee collaborates with scholars based in Singapore, South Korea and United States. Geunyoung Lee's co-authors include Tyler Hyungtaek Rim, Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Yih Chung Tham, Tien Yin Wong, Sung Soo Kim, Ching‐Yu Cheng and Marco Yu and has published in prestigious journals such as Circulation, British Journal of Ophthalmology and BMC Medicine.

In The Last Decade

Geunyoung Lee

20 papers receiving 453 citations

Peers

Geunyoung Lee
Josef Huemer United Kingdom
Daniel Ting Singapore
Saad Khan United Kingdom
Hon Tym Wong Singapore
Ryan T. Yanagihara United States
Dejiang Xu Singapore
Sunny Virmani United States
In Sik Lee South Korea
Jaemin Son South Korea
Zhi Wei Lim Singapore
Josef Huemer United Kingdom
Geunyoung Lee
Citations per year, relative to Geunyoung Lee Geunyoung Lee (= 1×) peers Josef Huemer

Countries citing papers authored by Geunyoung Lee

Since Specialization
Citations

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

Fields of papers citing papers by Geunyoung Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Geunyoung Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Geunyoung Lee. A scholar is included among the top collaborators of Geunyoung Lee 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 Geunyoung Lee. Geunyoung Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nusinovici, Simon, Tyler Hyungtaek Rim, Hengtong Li, et al.. (2024). Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study. The Lancet Healthy Longevity. 5(10). 100593–100593. 15 indexed citations
2.
Kim, Hyeonmin, June‐Goo Lee, Geunyoung Lee, et al.. (2024). Deep learning model for intravascular ultrasound image segmentation with temporal consistency. The International Journal of Cardiovascular Imaging. 40(11). 2283–2292. 4 indexed citations
3.
Rim, Tyler Hyungtaek, Sungha Park, Sung Soo Kim, et al.. (2023). Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores. European Heart Journal - Digital Health. 4(3). 236–244. 15 indexed citations
4.
Lee, Chan Joo, Tyler Hyungtaek Rim, Hyun Goo Kang, et al.. (2023). Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI. Journal of the American Medical Informatics Association. 31(1). 130–138. 18 indexed citations
5.
Rim, Tyler Hyungtaek, Hyun Goo Kang, Chan Joo Lee, et al.. (2023). Abstract 16476: Association Between Atrial Fibrillation and Deep Learning Derived Retinal Features Trained on Electrocardiogram versus Coronary Artery Calcium. Circulation. 148(Suppl_1). 1 indexed citations
6.
Tseng, Rachel Marjorie Wei Wen, Tyler Hyungtaek Rim, Eduard Shantsila, et al.. (2023). Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank. BMC Medicine. 21(1). 28–28. 24 indexed citations
7.
Joo, Young Su, Tyler Hyungtaek Rim, Hee Byung Koh, et al.. (2023). Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors. npj Digital Medicine. 6(1). 114–114. 19 indexed citations
8.
Surya, Janani, et al.. (2023). Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma. Indian Journal of Ophthalmology. 71(8). 3039–3045. 6 indexed citations
9.
Nusinovici, Simon, Tyler Hyungtaek Rim, Marco Yu, et al.. (2022). Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk. Age and Ageing. 51(4). 51 indexed citations
10.
Betzler, Bjorn Kaijun, Sahil Thakur, Marco Yu, et al.. (2021). Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study. JMIR Medical Informatics. 9(8). e25165–e25165. 15 indexed citations
11.
Liu, Yu‐Chi, Tyler Hyungtaek Rim, Anandalakshmi Venkatraman, et al.. (2021). Automatic segmentation of corneal deposits from corneal stromal dystrophy images via deep learning. Computers in Biology and Medicine. 137. 104675–104675. 7 indexed citations
12.
Rim, Tyler Hyungtaek, Aaron Lee, Daniel Shu Wei Ting, et al.. (2021). Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography. British Journal of Ophthalmology. 106(9). 1301–1307. 6 indexed citations
13.
Ryu, Ik Hee, Geunyoung Lee, Jin Kuk Kim, et al.. (2021). Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens. Translational Vision Science & Technology. 10(6). 5–5. 37 indexed citations
14.
Rim, Tyler Hyungtaek, Zhi Da Soh, Yih Chung Tham, et al.. (2020). Deep Learning for Automated Sorting of Retinal Photographs. Ophthalmology Retina. 4(8). 793–800. 13 indexed citations
15.
Rim, Tyler Hyungtaek, Geunyoung Lee, Yih Chung Tham, et al.. (2020). Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. The Lancet Digital Health. 2(10). e526–e536. 92 indexed citations
16.
Rim, Tyler Hyungtaek, Aaron Lee, Daniel Shu Wei Ting, et al.. (2020). Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm. British Journal of Ophthalmology. 105(8). 1133–1139. 27 indexed citations
17.
Lee, Geunyoung, et al.. (2020). Enhanced Photocatalytic Property of TiO2 Treated with H2 at Ambient Pressure. Bulletin of the Korean Chemical Society. 41(12). 1130–1133. 1 indexed citations
18.
Rim, Tyler Hyungtaek, Yih Chung Tham, Tae Keun Yoo, et al.. (2020). Deep learning system differentiates ethnicities from fundus photographs of a multi-ethnic Asian population.. 61(7). 5248–5248. 1 indexed citations
19.
Yoo, Tae Keun, Ik Hee Ryu, Jin Kuk Kim, et al.. (2020). Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level. Translational Vision Science & Technology. 9(2). 8–8. 56 indexed citations
20.
Yoo, Tae Keun, Ik Hee Ryu, Geunyoung Lee, et al.. (2019). Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. npj Digital Medicine. 2(1). 59–59. 56 indexed citations

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Josef Huemer Breakdown of academic impact, for papers by Daniel Ting Breakdown of academic impact, for papers by Saad Khan Breakdown of academic impact, for papers by Hon Tym Wong Breakdown of academic impact, for papers by Ryan T. Yanagihara Breakdown of academic impact, for papers by Dejiang Xu Breakdown of academic impact, for papers by Sunny Virmani Breakdown of academic impact, for papers by In Sik Lee Breakdown of academic impact, for papers by Jaemin Son Breakdown of academic impact, for papers by Zhi Wei Lim
Rankless by CCL
2026