Yang Sheng

723 total citations
55 papers, 510 citations indexed

About

Yang Sheng is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Yang Sheng has authored 55 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Radiation, 31 papers in Radiology, Nuclear Medicine and Imaging and 21 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Yang Sheng's work include Advanced Radiotherapy Techniques (39 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and Medical Imaging Techniques and Applications (10 papers). Yang Sheng is often cited by papers focused on Advanced Radiotherapy Techniques (39 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and Medical Imaging Techniques and Applications (10 papers). Yang Sheng collaborates with scholars based in United States, China and Hong Kong. Yang Sheng's co-authors include F Yin, Qiuwen Wu, Yaorong Ge, Jiahan Zhang, Chunhao Wang, Taoran Li, L. Yuan, Manisha Palta, Christopher G. Willett and Yushi Chang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Yang Sheng

50 papers receiving 510 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yang Sheng United States 15 394 333 195 96 69 55 510
Rachel McCarroll United States 10 323 0.8× 339 1.0× 138 0.7× 101 1.1× 62 0.9× 19 475
L. Yuan United States 12 497 1.3× 341 1.0× 314 1.6× 106 1.1× 35 0.5× 36 600
J. van der Stoep Netherlands 7 350 0.9× 451 1.4× 308 1.6× 122 1.3× 61 0.9× 13 613
H. Geng United States 10 255 0.6× 288 0.9× 184 0.9× 83 0.9× 55 0.8× 26 509
Anna M. Dinkla Netherlands 7 363 0.9× 390 1.2× 144 0.7× 129 1.3× 57 0.8× 13 526
A. Delaney Netherlands 10 537 1.4× 348 1.0× 336 1.7× 99 1.0× 51 0.7× 15 638
Jianrong Dai China 11 443 1.1× 483 1.5× 241 1.2× 171 1.8× 53 0.8× 44 616
Brent van der Heyden Netherlands 12 306 0.8× 407 1.2× 219 1.1× 211 2.2× 60 0.9× 33 591
Jaehee Chun South Korea 12 230 0.6× 323 1.0× 88 0.5× 100 1.0× 86 1.2× 28 438
Nataliya Kovalchuk United States 13 276 0.7× 275 0.8× 177 0.9× 59 0.6× 23 0.3× 64 484

Countries citing papers authored by Yang Sheng

Since Specialization
Citations

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

Fields of papers citing papers by Yang Sheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yang Sheng

This figure shows the co-authorship network connecting the top 25 collaborators of Yang Sheng. A scholar is included among the top collaborators of Yang Sheng 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 Yang Sheng. Yang Sheng 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.
2.
Du, Hongfang, Lijing Wang, Siyuan Cheng, et al.. (2025). The surface binding and energy issues in the rational design of separators for Li||S batteries. Journal of Energy Chemistry. 112. 987–1013. 1 indexed citations
3.
Huang, Wenhan, et al.. (2025). Resveratrol Attenuates Inflammation in Acute Lung Injury through ROS-Triggered TXNIP/NLRP3 Pathway. Chinese Journal of Integrative Medicine. 31(12). 1078–1086. 1 indexed citations
4.
Sheng, Yang, et al.. (2024). A reinforcement learning agent for head and neck intensity-modulated radiation therapy. Frontiers in Physics. 12. 4 indexed citations
5.
Chen, Zhi, Yuhua Huang, Bing Li, et al.. (2024). Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization. Physics in Medicine and Biology. 69(15). 155007–155007. 1 indexed citations
6.
Xie, Yibo, et al.. (2024). Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT). Physics in Medicine and Biology. 70(1). 15010–15010. 3 indexed citations
7.
Guo, Zuyu, et al.. (2022). Researches advanced in application of medical image analysis based on deep learning. UA Campus Repository (The University of Arizona). 89–89. 1 indexed citations
8.
Ge, Yaorong, et al.. (2022). Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy. Physics in Medicine and Biology. 67(21). 215009–215009. 1 indexed citations
9.
Sheng, Yang, et al.. (2021). A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning. Quantitative Imaging in Medicine and Surgery. 11(12). 4797–4806. 1 indexed citations
10.
Yoo, Sua, Yang Sheng, Rachel Blitzblau, et al.. (2021). Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy. Advances in Radiation Oncology. 6(2). 100656–100656. 7 indexed citations
11.
Sheng, Yang, Manisha Palta, Brian G. Czito, et al.. (2021). Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy. Physics in Medicine and Biology. 66(24). 245002–245002. 6 indexed citations
12.
Xie, Yibo, Ruilin Li, F Yin, et al.. (2021). Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings—a phantom simulation approach. Quantitative Imaging in Medicine and Surgery. 11(12). 4835–4846. 1 indexed citations
13.
Wu, Qiuwen, et al.. (2021). Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning. Physics in Medicine and Biology. 66(23). 23NT01–23NT01. 1 indexed citations
14.
Wu, Yue, et al.. (2020). Current Therapeutic Progress of CDK4/6 Inhibitors in Breast Cancer. SHILAP Revista de lepidopterología. 4 indexed citations
15.
Wang, Chunhao, Chenyang Liu, Yushi Chang, et al.. (2020). Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application. Frontiers in Oncology. 10. 1592–1592. 23 indexed citations
16.
Sheng, Yang, Yaorong Ge, Chris R. Kelsey, et al.. (2020). Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study. Frontiers in Artificial Intelligence. 3. 66–66. 5 indexed citations
17.
Yuan, L., Wei Zhu, Yaorong Ge, et al.. (2018). Lung IMRT planning with automatic determination of beam angle configurations. Physics in Medicine and Biology. 63(13). 135024–135024. 12 indexed citations
18.
Zhang, Jiahan, et al.. (2018). An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning. Frontiers in Oncology. 8. 57–57. 31 indexed citations
19.
Sheng, Yang, Taoran Li, You Zhang, et al.. (2015). Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning. Physics in Medicine and Biology. 60(18). 7277–7291. 22 indexed citations
20.
Yuan, L., Qiuwen Wu, F Yin, et al.. (2015). Standardized beam bouquets for lung IMRT planning. Physics in Medicine and Biology. 60(5). 1831–1843. 19 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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