Kuo Men

2.0k total citations
85 papers, 1.4k citations indexed

About

Kuo Men is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Kuo Men has authored 85 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Radiology, Nuclear Medicine and Imaging, 66 papers in Radiation and 33 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Kuo Men's work include Advanced Radiotherapy Techniques (65 papers), Medical Imaging Techniques and Applications (43 papers) and Radiomics and Machine Learning in Medical Imaging (26 papers). Kuo Men is often cited by papers focused on Advanced Radiotherapy Techniques (65 papers), Medical Imaging Techniques and Applications (43 papers) and Radiomics and Machine Learning in Medical Imaging (26 papers). Kuo Men collaborates with scholars based in China, United States and Hong Kong. Kuo Men's co-authors include Ye‐Xiong Li, Jianrong Dai, Junlin Yi, Jianrong Dai, Xinyuan Chen, Jianrong Dai, Xinyuan Chen, H. Geng, Ying Xiao and Shulian Wang and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Kuo Men

77 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kuo Men China 16 1.1k 793 379 364 214 85 1.4k
Mark J. Gooding United Kingdom 21 1.1k 1.0× 781 1.0× 378 1.0× 453 1.2× 252 1.2× 64 1.6k
Michael Jameson Australia 19 938 0.9× 882 1.1× 482 1.3× 210 0.6× 99 0.5× 76 1.3k
Yong Yin China 21 1.1k 1.0× 608 0.8× 799 2.1× 255 0.7× 145 0.7× 195 1.8k
Linghong Zhou China 19 854 0.8× 580 0.7× 307 0.8× 421 1.2× 167 0.8× 71 1.2k
Neelam Tyagi United States 23 1.2k 1.1× 900 1.1× 551 1.5× 290 0.8× 133 0.6× 82 1.6k
Charlotte L. Brouwer Netherlands 21 1.2k 1.1× 1.1k 1.4× 634 1.7× 392 1.1× 152 0.7× 54 1.9k
Jun Zhou United States 22 933 0.9× 820 1.0× 751 2.0× 463 1.3× 139 0.6× 121 1.6k
Michalis Aristophanous United States 19 773 0.7× 642 0.8× 454 1.2× 164 0.5× 65 0.3× 51 1.0k
Lisanne V. van Dijk Netherlands 23 1.3k 1.1× 669 0.8× 641 1.7× 279 0.8× 149 0.7× 89 1.9k
Hidetaka Arimura Japan 19 819 0.7× 181 0.2× 547 1.4× 223 0.6× 245 1.1× 123 1.3k

Countries citing papers authored by Kuo Men

Since Specialization
Citations

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

Fields of papers citing papers by Kuo Men

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kuo Men

This figure shows the co-authorship network connecting the top 25 collaborators of Kuo Men. A scholar is included among the top collaborators of Kuo Men 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 Kuo Men. Kuo Men 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.
Ma, Xiangyu, Yuchao Ma, Yu Wang, et al.. (2025). Contrast-enhanced image synthesis using latent diffusion model for precise online tumor delineation in MRI-guided adaptive radiotherapy for brain metastases. Physics in Medicine and Biology. 70(13). 135012–135012.
2.
Xu, Yuan, et al.. (2025). A feasibility study of deep learning prediction model for VMAT patient-specific QA. Frontiers in Oncology. 15. 1509449–1509449. 1 indexed citations
3.
4.
Xu, Yuan, et al.. (2025). Diagnostic image‐based treatment planning for online adaptive ultra‐hypofractionated prostate cancer radiotherapy with MR‐Linac. Journal of Applied Clinical Medical Physics. 26(6). e70075–e70075. 1 indexed citations
5.
Wang, Ningyu, Jiawei Fan, Yingjie Xu, et al.. (2024). Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer. Physica Medica. 124. 104492–104492. 3 indexed citations
6.
Ren, Wenting, Bin Liang, Chao Sun, et al.. (2024). A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma. Physica Medica. 121. 103362–103362. 1 indexed citations
7.
8.
Chen, Xinyuan, et al.. (2023). Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning. Frontiers in Oncology. 13. 1041769–1041769. 9 indexed citations
9.
Ren, Wenting, et al.. (2023). Is it necessary to perform measurement‐based patient‐specific quality assurance for online adaptive radiotherapy with Elekta Unity MR‐Linac?. Journal of Applied Clinical Medical Physics. 25(2). e14175–e14175. 4 indexed citations
10.
Ma, Min, Hui Yan, Minghui Li, et al.. (2023). Determining the quality control frequency of an MR‐linac using risk matrix (RM) analysis. Journal of Applied Clinical Medical Physics. 24(8). e13984–e13984. 2 indexed citations
11.
12.
Wei, Ran, et al.. (2023). A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis. Medical Physics. 51(4). 2695–2706. 3 indexed citations
13.
Chen, Xinyuan, et al.. (2023). Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma. Radiation Oncology. 18(1). 182–182. 6 indexed citations
14.
Zhu, Ji, Xinyuan Chen, Yuxiang Liu, et al.. (2023). Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method. Radiation Oncology. 18(1). 108–108. 7 indexed citations
15.
Liu, Xin, Yong Yang, Shulian Wang, et al.. (2022). Effects of gross tumor volume and radiation dose on survival and locoregional recurrence in early-stage extranodal NK/T-cell lymphoma treated with intensity-modulated radiation therapy. Journal of Cancer Research and Clinical Oncology. 149(8). 5219–5230. 2 indexed citations
16.
Liu, Zhiqiang, Kuo Men, Wenqing Wang, et al.. (2022). Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model. Frontiers in Oncology. 12. 889266–889266. 6 indexed citations
17.
Men, Kuo, Xinyuan Chen, Ji Zhu, et al.. (2021). Automatic segmentation of three clinical target volumes in radiotherapy using lifelong learning. Radiotherapy and Oncology. 157. 1–7. 13 indexed citations
18.
Xu, Yuan, Zhihui Hu, Yuan Tian, et al.. (2021). Non-coplanar volumetric modulated arc therapy for locoregional radiotherapy of left-sided breast cancer including internal mammary nodes. Radiology and Oncology. 55(4). 499–507. 7 indexed citations
19.
Xu, Yingjie, Qingfeng Liu, Xinyuan Chen, et al.. (2021). Longitudinal Grouping of Target Volumes for Volumetric-Modulated Arc Therapy of Multiple Brain Metastases. Frontiers in Oncology. 11. 578934–578934. 2 indexed citations
20.

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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