Shaoyan Pan

719 total citations
35 papers, 372 citations indexed

About

Shaoyan Pan is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shaoyan Pan has authored 35 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Biomedical Engineering and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shaoyan Pan's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Medical Imaging Techniques and Applications (11 papers) and Advanced X-ray and CT Imaging (10 papers). Shaoyan Pan is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Medical Imaging Techniques and Applications (11 papers) and Advanced X-ray and CT Imaging (10 papers). Shaoyan Pan collaborates with scholars based in United States, Spain and Canada. Shaoyan Pan's co-authors include Xiaofeng Yang, Chih‐Wei Chang, Justin Roper, Tonghe Wang, Richard L. J. Qiu, Pretesh Patel, Jacob Wynne, Junbo Peng, David S. Yu and Yang Lei and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging.

In The Last Decade

Shaoyan Pan

28 papers receiving 361 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shaoyan Pan United States 12 258 121 88 70 67 35 372
Nuo Tong China 7 206 0.8× 127 1.0× 67 0.8× 60 0.9× 105 1.6× 16 309
Gisèle Pereira United States 8 257 1.0× 103 0.9× 43 0.5× 63 0.9× 102 1.5× 12 359
Xudong Xue China 9 155 0.6× 65 0.5× 44 0.5× 54 0.8× 65 1.0× 26 245
A. Barateau France 10 350 1.4× 150 1.2× 69 0.8× 34 0.5× 291 4.3× 36 452
Frank Zijlstra Netherlands 11 321 1.2× 129 1.1× 55 0.6× 25 0.4× 149 2.2× 25 454
Mateusz C. Florkow Netherlands 9 239 0.9× 132 1.1× 56 0.6× 27 0.4× 107 1.6× 12 427
Hajar Emami United States 5 193 0.7× 75 0.6× 85 1.0× 50 0.7× 94 1.4× 7 283
Enzo Battistella France 6 208 0.8× 76 0.6× 34 0.4× 41 0.6× 24 0.4× 14 276
Donghwi Hwang South Korea 9 484 1.9× 236 2.0× 110 1.3× 32 0.5× 156 2.3× 13 588
Umair Javaid Belgium 6 149 0.6× 55 0.5× 28 0.3× 86 1.2× 40 0.6× 11 287

Countries citing papers authored by Shaoyan Pan

Since Specialization
Citations

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

Fields of papers citing papers by Shaoyan Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaoyan Pan

This figure shows the co-authorship network connecting the top 25 collaborators of Shaoyan Pan. A scholar is included among the top collaborators of Shaoyan Pan 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 Shaoyan Pan. Shaoyan Pan 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.
Pan, Shaoyan, Junbo Peng, Yuan Gao, et al.. (2025). CBCT Reconstruction Using Single X-Ray Projection With Cycle-Domain Geometry-Integrated Denoising Diffusion Probabilistic Models. IEEE Transactions on Medical Imaging. 44(7). 2933–2947.
2.
Pan, Shaoyan, et al.. (2025). Self‐supervised adversarial diffusion models for fast MRI reconstruction. Medical Physics. 52(6). 3888–3899. 2 indexed citations
3.
Pan, Shaoyan, et al.. (2025). Cycle-guided denoising diffusion probability model for 3D cross-modality MRI synthesis. 70–70. 1 indexed citations
4.
Chang, Chih‐Wei, Junbo Peng, Shaoyan Pan, et al.. (2024). High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling. Physics in Medicine and Biology. 69(4). 45001–45001. 17 indexed citations
5.
6.
Li, Yuheng, Jacob Wynne, Jing Wang, et al.. (2024). Cross‐shaped windows transformer with self‐supervised pretraining for clinically significant prostate cancer detection in bi‐parametric MRI. Medical Physics. 52(2). 993–1004. 5 indexed citations
7.
Pan, Shaoyan, Elham Abouei, Jacob Wynne, et al.. (2024). Synthetic CT generation from MRI using 3D diffusion model. 91–91. 2 indexed citations
8.
Pan, Shaoyan, Elham Abouei, Junbo Peng, et al.. (2024). Full-dose PET synthesis from low-dose PET using 2D high efficiency denoising diffusion probabilistic model. 65–65. 3 indexed citations
9.
Pan, Shaoyan, et al.. (2024). Advancing medical imaging with language models: featuring a spotlight on ChatGPT. Physics in Medicine and Biology. 69(10). 10TR01–10TR01. 15 indexed citations
10.
Pan, Shaoyan, Chih‐Wei Chang, Jun Zhou, et al.. (2024). Exploring dual energy CT synthesis in CBCT-based adaptive radiotherapy and proton therapy: application of denoising diffusion probabilistic models. Physics in Medicine and Biology. 69(21). 215011–215011.
11.
Pan, Shaoyan, Chaoqiong Ma, Chih‐Wei Chang, et al.. (2023). Multi-structure segmentation on cardiac MRI using multilayer perceptron mixer network. 11–11. 1 indexed citations
12.
Pan, Shaoyan, Yiqiao Liu, Michal R. Tomaszewski, et al.. (2023). Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations. 1–5.
13.
Pan, Shaoyan, Jacob Wynne, Mingzhe Hu, et al.. (2023). Multi-organ CT segmentation using shifted-window multilayer perceptron mixer. 62. 62–62. 2 indexed citations
14.
Gao, Yuan, Chih‐Wei Chang, Justin Roper, et al.. (2023). Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method. Frontiers in Oncology. 13. 1278180–1278180. 4 indexed citations
15.
Li, Yuheng, Boran Zhou, Jing Wang, et al.. (2023). Ultrasound-based dominant intraprostatic lesion classification with Swin Transformer. 31–31. 2 indexed citations
16.
Pan, Shaoyan, Tonghe Wang, Richard L. J. Qiu, et al.. (2023). 2D medical image synthesis using transformer-based denoising diffusion probabilistic model. Physics in Medicine and Biology. 68(10). 105004–105004. 67 indexed citations
17.
Wynne, Jacob, Yang Lei, Shaoyan Pan, et al.. (2023). Rapid unpaired CBCT‐based synthetic CT for CBCT‐guided adaptive radiotherapy. Journal of Applied Clinical Medical Physics. 24(10). e14064–e14064. 5 indexed citations
18.
Gao, Yuan, Chih‐Wei Chang, Shaoyan Pan, et al.. (2023). Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy. Physics in Medicine and Biology. 69(2). 25004–25004. 14 indexed citations
19.
Chang, Chih‐Wei, Yuan Gao, Tonghe Wang, et al.. (2022). Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning. Physics in Medicine and Biology. 67(11). 115010–115010. 24 indexed citations
20.
Pan, Shaoyan, Yang Lei, Tonghe Wang, et al.. (2022). Male pelvic multi-organ segmentation using token-based transformer Vnet. Physics in Medicine and Biology. 67(20). 205012–205012. 26 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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