Shujun Liang

689 total citations
20 papers, 467 citations indexed

About

Shujun Liang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Shujun Liang has authored 20 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Artificial Intelligence. Recurrent topics in Shujun Liang's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (6 papers) and Head and Neck Cancer Studies (5 papers). Shujun Liang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (6 papers) and Head and Neck Cancer Studies (5 papers). Shujun Liang collaborates with scholars based in China, United States and South Korea. Shujun Liang's co-authors include Yu Zhang, Dinggang Shen, Tao Zhong, Xia Huang, Xinrui Yuan, Shangqing Liu, Tianjiao Liu, Qianqian Guo, Weidong Sun and Chunfeng Lian and has published in prestigious journals such as NeuroImage, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Shujun Liang

19 papers receiving 451 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shujun Liang China 9 309 182 105 75 72 20 467
Jincao Yao China 10 247 0.8× 156 0.9× 53 0.5× 12 0.2× 33 0.5× 68 424
Zohaib Salahuddin Netherlands 7 274 0.9× 173 1.0× 59 0.6× 13 0.2× 85 1.2× 13 446
Marco Caballo Netherlands 13 284 0.9× 113 0.6× 17 0.2× 26 0.3× 102 1.4× 30 375
Lars Johannes Isaksson Italy 8 133 0.4× 41 0.2× 14 0.1× 36 0.5× 39 0.5× 10 235
Shangqing Liu China 7 265 0.9× 124 0.7× 161 1.5× 55 0.7× 87 1.2× 14 414
Xia Huang China 9 267 0.9× 82 0.5× 83 0.8× 65 0.9× 66 0.9× 15 387
Navdeep Dahiya India 10 137 0.4× 65 0.4× 78 0.7× 18 0.2× 70 1.0× 25 377
Jeremy Webb United States 10 234 0.8× 148 0.8× 43 0.4× 4 0.1× 109 1.5× 20 357
Joanne Hoffman United States 5 259 0.8× 126 0.7× 98 0.9× 15 0.2× 97 1.3× 7 415

Countries citing papers authored by Shujun Liang

Since Specialization
Citations

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

Fields of papers citing papers by Shujun Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shujun Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Shujun Liang. A scholar is included among the top collaborators of Shujun Liang 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 Shujun Liang. Shujun Liang 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.
Zhong, Tao, Shujun Liang, Zhenyuan Ning, et al.. (2024). nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species. NeuroImage. 295. 120652–120652.
2.
Ye, Feng, et al.. (2024). A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma. Frontiers in Oncology. 14. 1377366–1377366. 1 indexed citations
3.
Liu, Jinyu, et al.. (2024). Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma. IEEE Transactions on Medical Imaging. 43(11). 3676–3689. 2 indexed citations
4.
Chen, Gengdong, Shujun Liang, Lan Huang, et al.. (2023). Associations of Dietary Anthocyanidins Intake with Bone Health in Children: A Cross-Sectional Study. Calcified Tissue International. 113(4). 393–402. 3 indexed citations
5.
6.
Wang, Zhaojie, Dongli Li, Lijun Mo, et al.. (2022). Low-dose cadmium exposure promotes osteoclastogenesis by enhancing autophagy via inhibiting the mTOR/p70S6K1 signaling pathway. Toxicology Letters. 367. 9–18. 8 indexed citations
7.
Liang, Shujun, et al.. (2022). A multi-perspective information aggregation network for automated T-staging detection of nasopharyngeal carcinoma. Physics in Medicine and Biology. 67(24). 245007–245007. 8 indexed citations
8.
Chen, Shanshan, Xuanrui Zhang, Yuanhuan Wei, et al.. (2022). The Relationship between Dietary Pattern and Bone Mass in School-Age Children. Nutrients. 14(18). 3752–3752. 7 indexed citations
9.
Chen, Gengdong, Yan Li, Shujun Liang, et al.. (2021). Associations of dietary anthocyanidins intake with body composition in Chinese children: a cross-sectional study. Food & Nutrition Research. 65. 4 indexed citations
10.
11.
Liang, Shujun, et al.. (2021). Breast ultrasound image segmentation: A coarse‐to‐fine fusion convolutional neural network. Medical Physics. 48(8). 4262–4278. 36 indexed citations
12.
Shi, Jun, Linlin Wang, Shanshan Wang, et al.. (2020). Applications of deep learning in medical imaging: a survey. Journal of Image and Graphics. 25(10). 1953–1981. 8 indexed citations
13.
Liang, Shujun, Kim‐Han Thung, Dong Nie, Yu Zhang, & Dinggang Shen. (2020). Multi-View Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at Risk in Head and Neck CT Images. IEEE Transactions on Medical Imaging. 39(9). 2794–2805. 29 indexed citations
14.
Liu, Tianjiao, Qianqian Guo, Chunfeng Lian, et al.. (2019). Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Medical Image Analysis. 58. 101555–101555. 141 indexed citations
15.
Tang, Fan, Shujun Liang, Tao Zhong, et al.. (2019). Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs. European Radiology. 30(2). 823–832. 25 indexed citations
16.
Zhong, Tao, Xia Huang, Fan Tang, et al.. (2019). Boosting‐based cascaded convolutional neural networks for the segmentation of CT organs‐at‐risk in nasopharyngeal carcinoma. Medical Physics. 46(12). 5602–5611. 30 indexed citations
17.
Liu, Shangqing, et al.. (2019). Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning. IEEE Access. 8. 2906–2914. 47 indexed citations
18.
Liang, Shujun, Fan Tang, Xia Huang, et al.. (2018). Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. European Radiology. 29(4). 1961–1967. 106 indexed citations
19.
Yang, Feng, et al.. (2017). [A probability model for analyzing speckles in intravascular ultrasound images to facilitate image segmentation].. PubMed. 37(11). 1476–1483. 1 indexed citations
20.
Liang, Shujun, et al.. (2017). Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling. BMC Medical Imaging. 17(1). 57–57. 8 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Jincao Yao Breakdown of academic impact, for papers by Zohaib Salahuddin Breakdown of academic impact, for papers by Marco Caballo Breakdown of academic impact, for papers by Lars Johannes Isaksson Breakdown of academic impact, for papers by Shangqing Liu Breakdown of academic impact, for papers by Xia Huang Breakdown of academic impact, for papers by Navdeep Dahiya Breakdown of academic impact, for papers by Jeremy Webb Breakdown of academic impact, for papers by Joanne Hoffman
Rankless by CCL
2026