Yu‐Chun Lin

1.6k total citations
75 papers, 1.2k citations indexed

About

Yu‐Chun Lin is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Yu‐Chun Lin has authored 75 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Cardiology and Cardiovascular Medicine and 10 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Yu‐Chun Lin's work include MRI in cancer diagnosis (18 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Advanced MRI Techniques and Applications (13 papers). Yu‐Chun Lin is often cited by papers focused on MRI in cancer diagnosis (18 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Advanced MRI Techniques and Applications (13 papers). Yu‐Chun Lin collaborates with scholars based in Taiwan, United States and Japan. Yu‐Chun Lin's co-authors include Shu‐Hang Ng, Jiun‐Jie Wang, Gigin Lin, Sheung‐Fat Ko, Tzu‐Chen Yen, Yau‐Yau Wai, Hon‐Kan Yip, Cheuk‐Kwan Sun, Ji‐Hong Hong and Sarah Chua and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and The Journal of Physiology.

In The Last Decade

Yu‐Chun Lin

68 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yu‐Chun Lin Taiwan 20 534 224 170 138 135 75 1.2k
Jared R. Robbins United States 20 181 0.3× 228 1.0× 316 1.9× 66 0.5× 101 0.7× 64 1.3k
Kevin L. Berger United States 14 456 0.9× 203 0.9× 178 1.0× 53 0.4× 31 0.2× 25 950
Shinya Fujii Japan 26 911 1.7× 353 1.6× 197 1.2× 90 0.7× 78 0.6× 106 2.1k
Shingo Baba Japan 26 984 1.8× 518 2.3× 499 2.9× 211 1.5× 297 2.2× 142 2.5k
Sun‐Won Park South Korea 28 509 1.0× 446 2.0× 259 1.5× 28 0.2× 148 1.1× 80 1.8k
Francesco Garaci Italy 24 552 1.0× 169 0.8× 178 1.0× 46 0.3× 404 3.0× 126 1.8k
Andrea Elefante Italy 21 256 0.5× 289 1.3× 127 0.7× 13 0.1× 95 0.7× 108 1.3k
Wu‐Chung Shen Taiwan 20 185 0.3× 293 1.3× 128 0.8× 31 0.2× 121 0.9× 68 1.1k
Gregory G. Heuer United States 30 186 0.3× 840 3.8× 158 0.9× 47 0.3× 368 2.7× 148 2.9k
Stefan C. A. Steens Netherlands 22 338 0.6× 112 0.5× 133 0.8× 27 0.2× 79 0.6× 50 1.2k

Countries citing papers authored by Yu‐Chun Lin

Since Specialization
Citations

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

Fields of papers citing papers by Yu‐Chun Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu‐Chun Lin. 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 Yu‐Chun Lin. The network helps show where Yu‐Chun Lin may publish in the future.

Co-authorship network of co-authors of Yu‐Chun Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Yu‐Chun Lin. A scholar is included among the top collaborators of Yu‐Chun Lin 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 Yu‐Chun Lin. Yu‐Chun Lin 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.
Chai, Wen‐Yen, Gigin Lin, Chao‐Jan Wang, et al.. (2025). A Deep Learning‐Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients. Journal of Magnetic Resonance Imaging. 63(1). 264–276.
4.
Wu, Ren‐Chin, Yu‐Chun Lin, Yen-Ling Huang, et al.. (2024). Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism. Cancer Imaging. 24(1). 112–112.
5.
Lin, Yu‐Chun, Ren‐Chin Wu, Hsin‐Ying Lu, et al.. (2023). Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics. 13(24). 3632–3632.
6.
Chiang, Yu‐Ting, Yingyu Wu, Yu‐Chun Lin, Yu‐Yao Huang, & Juu‐Chin Lu. (2023). Cyclodextrin-Mediated Cholesterol Depletion Induces Adiponectin Secretion in 3T3-L1 Adipocytes. International Journal of Molecular Sciences. 24(19). 14718–14718. 1 indexed citations
7.
Lin, Yuhan, et al.. (2023). Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan. European Journal of Radiology Open. 12. 100534–100534.
8.
Lin, Yu‐Chun, Yen-Ling Huang, Hsin‐Ying Lu, et al.. (2023). Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging. 14(1). 14–14. 11 indexed citations
9.
Lin, Gigin, Yu‐Hsiang Juan, Chao‐Hung Wang, et al.. (2020). Left Ventricular Function and Myocardial Triglyceride Content on 3T Cardiac MR Predict Major Cardiovascular Adverse Events and Readmission in Patients Hospitalized with Acute Heart Failure. Journal of Clinical Medicine. 9(1). 169–169. 11 indexed citations
10.
Tsai, Chih‐Chien, Shu‐Hang Ng, Yao-Liang Chen, et al.. (2020). T1 and T2∗ relaxation time in the parcellated myocardium of healthy Taiwanese participants: A single center study. Biomedical Journal. 44(6). S132–S143. 2 indexed citations
11.
Yang, Lan‐Yan, Yu‐Chun Lin, See‐Tong Pang, et al.. (2018). Metabolic Volumetric Parameters in 11C-Choline PET/MR Are Superior PET Imaging Biomarkers for Primary High-Risk Prostate Cancer. Contrast Media & Molecular Imaging. 2018. 1–10. 9 indexed citations
13.
Tsai, Hui-Ju, Kun‐Yi Chien, M. S. Shih, et al.. (2017). Functional links between Disabled‐2 Ser723 phosphorylation and thrombin signaling in human platelets. Journal of Thrombosis and Haemostasis. 15(10). 2029–2044. 11 indexed citations
14.
Lin, Gigin, Chyong‐Huey Lai, Shang‐Yueh Tsai, et al.. (2016). 1H MR spectroscopy in cervical carcinoma using external phase array body coil at 3.0 Tesla: Prediction of poor prognostic human papillomavirus genotypes. Journal of Magnetic Resonance Imaging. 45(3). 899–907. 12 indexed citations
15.
Cheng, Cheng‐I, Yu‐Chun Lin, Tzu‐Hsien Tsai, et al.. (2014). The Prognostic Values of Leukocyte Rho Kinase Activity in Acute Ischemic Stroke. BioMed Research International. 2014. 1–11. 9 indexed citations
18.
Lin, Yu‐Chun, Chi‐Chih Wang, Y Y Wai, et al.. (2009). Significant Temporal Evolution of Diffusion Anisotropy for Evaluating Early Response to Radiosurgery in Patients with Vestibular Schwannoma: Findings from Functional Diffusion Maps. American Journal of Neuroradiology. 31(2). 269–274. 13 indexed citations
19.
Wang, Jiun‐Jie, Yu‐Chun Lin, Yau‐Yau Wai, et al.. (2008). Visualization of the coherence of the principal diffusion orientation: An eigenvector‐based approach. Magnetic Resonance in Medicine. 59(4). 764–770. 11 indexed citations
20.
Nakano, Toshiaki, Chao‐Long Chen, Shigeru Goto, et al.. (2006). The immunological role of lipid transfer/metabolic proteins in liver transplantation tolerance. Transplant Immunology. 17(2). 130–136. 2 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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