Minho Lee

529 total citations
30 papers, 323 citations indexed

About

Minho Lee is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience. According to data from OpenAlex, Minho Lee has authored 30 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 10 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Cognitive Neuroscience. Recurrent topics in Minho Lee's work include Dementia and Cognitive Impairment Research (8 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Neurological Disease Mechanisms and Treatments (5 papers). Minho Lee is often cited by papers focused on Dementia and Cognitive Impairment Research (8 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Neurological Disease Mechanisms and Treatments (5 papers). Minho Lee collaborates with scholars based in South Korea, United States and Italy. Minho Lee's co-authors include Qing Zhang, Donghyeon Kim, Regina E. Y. Kim, Hyun Kook Lim, Jee Young Kim, Dong Woo Kang, Sheng‐Min Wang, Min Kyoung Lee, Hyelin Na and Sung-Hye Kim and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Image Processing and Biochemical Pharmacology.

In The Last Decade

Minho Lee

25 papers receiving 313 citations

Peers

Minho Lee

PMH

CVPR

NEURO

CMN

Claudio Stamile France

Leonie Henschel Germany

İrem Taşçı Türkiye

Michael I. Miller United States

Wenliang Che China

Xiaoqing Zhou China

Zijian Wang China

Zhanxiong Wu China

Jeremy Bockholt United States

Claudio Stamile France

Minho Lee

88 ×0.9

40 ×0.6

PMH

62 ×1.0

CVPR

63 ×1.0

NEURO

28 ×0.5

CMN

Citations per year, relative to Minho Lee Minho Lee (= 1×) peers Claudio Stamile

Countries citing papers authored by Minho Lee

Since Specialization

Citations

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

Fields of papers citing papers by Minho Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minho Lee

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

All Works

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20 of 20 papers shown

Moon, Hojin, et al.. (2025). AI-Guided Chemotherapy Optimization in Lung Cancer Using Genomic and Survival Data. Journal of Personalized Medicine. 15(6). 218–218. 1 indexed citations

Choe, Yeong Sim, Regina E. Y. Kim, Jee Young Kim, et al.. (2024). Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation. Journal of Alzheimer s Disease Reports. 8(1). 863–876.

Lee, Hyunji, Minho Lee, Donghyeon Kim, et al.. (2024). Evaluating brain volume segmentation accuracy and reliability of FreeSurfer and Neurophet AQUA at variations in MRI magnetic field strengths. Scientific Reports. 14(1). 24513–24513. 2 indexed citations

Lee, Minwoo, Yeong Sim Choe, Hyunji Lee, et al.. (2024). A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. Scientific Reports. 14(1). 12276–12276. 10 indexed citations

Moon, Hojin, et al.. (2024). Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine. Cancer Informatics. 23. 2683898957–2683898957. 2 indexed citations

Park, Sung‐Hwan, Hyun Gi Kim, Minho Lee, et al.. (2023). A regional brain volume–based age prediction model for neonates and the derived brain maturation index. European Radiology. 34(6). 3892–3902.

Kim, Regina E. Y., Minho Lee, Dong Woo Kang, et al.. (2023). Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest. Journal of Alzheimer s Disease. 97(1). 259–271. 2 indexed citations

Kim, Regina E. Y., et al.. (2023). A Fully Automated Visual Grading System for White Matter Hyperintensities of T2-Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging. Journal of Integrative Neuroscience. 22(3). 57–57. 5 indexed citations

Kim, Regina E. Y., Minho Lee, Dong Woo Kang, et al.. (2023). Effects of education mediated by brain size on regional brain volume in adults. Psychiatry Research Neuroimaging. 330. 111600–111600. 6 indexed citations

10.

Lee, Soojin, et al.. (2023). Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods. Brain Research Bulletin. 205. 110825–110825. 5 indexed citations

11.

Lee, Minho, et al.. (2022). Fine-grained brain tissue segmentation for brain modeling of stroke patient. Computers in Biology and Medicine. 153. 106472–106472. 16 indexed citations

12.

Kim, Jee Young, Regina E. Y. Kim, Minho Lee, et al.. (2021). Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI. Brain Sciences. 11(6). 720–720. 14 indexed citations

13.

Kim, Jee Young, Minho Lee, Min Kyoung Lee, et al.. (2021). Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern. Psychiatry Investigation. 18(1). 69–79. 21 indexed citations

14.

Kim, Regina E. Y., Minho Lee, Dong Woo Kang, et al.. (2020). Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI. Diagnostics. 11(1). 13–13. 13 indexed citations

15.

Lee, Minho, Jee Young Kim, Regina E. Y. Kim, et al.. (2020). Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI. Brain Sciences. 10(12). 974–974. 40 indexed citations

16.

Lee, Minho, et al.. (2014). Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT. Journal of Digital Imaging. 27(4). 538–547. 6 indexed citations

17.

Kang, Shinae, et al.. (2010). Regulation of Nur77 protein turnover through acetylation and deacetylation induced by p300 and HDAC1. Biochemical Pharmacology. 80(6). 867–873. 56 indexed citations

18.

Ban, Sang-Woo, et al.. (2009). Self-Motivated Autonomous Robot With A Trainable Selective Attention Model. Intelligent Automation & Soft Computing. 15(2). 131–149.

19.

Ban, Sang-Woo, et al.. (2006). Biologically Motivated Face Selective Attention System. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 4292–4298. 1 indexed citations

20.

Lee, Minho, et al.. (2004). The Emotional Problems of Family having Children with Disabilities in Daily Life. 특수교육재활과학연구. 43(1). 137–158. 1 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.

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