Xun Zhao

912 total citations
23 papers, 577 citations indexed

About

Xun Zhao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Xun Zhao has authored 23 papers receiving a total of 577 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Xun Zhao's work include Advanced Neural Network Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Head and Neck Cancer Studies (4 papers). Xun Zhao is often cited by papers focused on Advanced Neural Network Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Head and Neck Cancer Studies (4 papers). Xun Zhao collaborates with scholars based in China, Hong Kong and United States. Xun Zhao's co-authors include Weiwei Cui, Dik Lun Lee, Di Dong, Yu Li, Ying Shan, Lianzhen Zhong, Yang Li, Jie Tian, Jiabao Wang and Hong Shan and has published in prestigious journals such as JNCI Journal of the National Cancer Institute, Gut and IEEE Access.

In The Last Decade

Xun Zhao

23 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xun Zhao China 13 215 193 136 73 53 23 577
Fatima Rashid Sheykhahmad Iran 11 262 1.2× 129 0.7× 109 0.8× 9 0.1× 12 0.2× 21 639
Mark Marsden United States 9 55 0.3× 50 0.3× 170 1.3× 34 0.5× 126 2.4× 14 488
Marcelo Zanchetta do Nascimento Brazil 17 682 3.2× 508 2.6× 338 2.5× 23 0.3× 42 0.8× 91 997
Leandro Alves Neves Brazil 16 520 2.4× 390 2.0× 249 1.8× 10 0.1× 31 0.6× 85 780
Zhineng Chen China 17 373 1.7× 540 2.8× 318 2.3× 5 0.1× 196 3.7× 47 1.2k
Sourav Dutta India 11 132 0.6× 63 0.3× 12 0.1× 109 1.5× 39 0.7× 46 541
Vincent Andrearczyk Switzerland 13 276 1.3× 147 0.8× 338 2.5× 31 0.4× 74 1.4× 34 605
Suling Xu China 16 216 1.0× 100 0.5× 71 0.5× 6 0.1× 40 0.8× 53 763
Zhuochen Jin China 10 160 0.7× 180 0.9× 114 0.8× 6 0.1× 85 1.6× 14 480
Jie Tian China 16 324 1.5× 181 0.9× 765 5.6× 11 0.2× 371 7.0× 39 1.2k

Countries citing papers authored by Xun Zhao

Since Specialization
Citations

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

Fields of papers citing papers by Xun Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xun Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Xun Zhao. A scholar is included among the top collaborators of Xun Zhao 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 Xun Zhao. Xun Zhao 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.
Jin, Zhihua, et al.. (2025). JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery From Large-Scale Human-LLM Conversational Datasets. IEEE Transactions on Visualization and Computer Graphics. 31(10). 7923–7937. 1 indexed citations
2.
Lin, Da-Feng, Hailin Li, Ting Liu, et al.. (2024). Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma. JNCI Journal of the National Cancer Institute. 116(8). 1294–1302. 14 indexed citations
3.
4.
Deng, Jingyu, Di Dong, Zhaoxiang Ye, et al.. (2023). Deep learning‐based radiomics model can predict extranodal soft tissue metastasis in gastric cancer. Medical Physics. 51(1). 267–277. 9 indexed citations
5.
Zhao, Xun, Yu-Jing Liang, Xu Zhang, et al.. (2022). Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 49(8). 2972–2982. 29 indexed citations
6.
Miao, Zhuang, et al.. (2022). A Multiview Metric Learning Method for Few-Shot Fine-Grained Classification. IEEE Access. 10. 52782–52790. 1 indexed citations
7.
Yang, Shusheng, Xinggang Wang, Yu Li, et al.. (2022). Temporally Efficient Vision Transformer for Video Instance Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2875–2885. 44 indexed citations
8.
Zhao, Xun, et al.. (2022). Active Learning for Open-set Annotation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 41–49. 19 indexed citations
9.
Miao, Zhuang, Xun Zhao, Jiabao Wang, Yang Li, & Hang Li. (2021). Complemental Attention Multi-Feature Fusion Network for Fine-Grained Classification. IEEE Signal Processing Letters. 28. 1983–1987. 27 indexed citations
10.
Wang, Xiao, et al.. (2021). Semantic-Guided Relation Propagation Network for Few-shot Action Recognition. 816–825. 24 indexed citations
11.
Zhong, Lianzhen, Di Dong, Xueliang Fang, et al.. (2021). A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study. EBioMedicine. 70. 103522–103522. 87 indexed citations
12.
Kang, Kai, et al.. (2020). A New Benchmark for Instance-Level Image Classification. IEEE Access. 8. 70306–70315. 4 indexed citations
13.
Zhao, Xun, Weiwei Cui, Yanhong Wu, et al.. (2019). Oui! Outlier Interpretation on Multi‐dimensional Data via Visual Analytics. Computer Graphics Forum. 38(3). 213–224. 7 indexed citations
14.
Wang, Jiabao, Yang Li, Zhuang Miao, Xun Zhao, & Rui Zhang. (2019). Multi-Level Metric Learning Network for Fine-Grained Classification. IEEE Access. 7. 166390–166397. 7 indexed citations
15.
Li, Yang, et al.. (2019). Tiny Fusion: Tiny Deep Convolutional Neural Network for Real-time Image Fusion. 384–389. 1 indexed citations
16.
Zhao, Xun, et al.. (2018). iForest: Interpreting Random Forests via Visual Analytics. IEEE Transactions on Visualization and Computer Graphics. 25(1). 407–416. 136 indexed citations
17.
Wang, Yong, Conglei Shi, Yanhong Wu, et al.. (2018). Towards Easy Comparison of Local Businesses Using Online Reviews. Computer Graphics Forum. 37(3). 63–74. 10 indexed citations
18.
Li, Yuanyuan, Shunjie Liu, Xun Zhao, et al.. (2017). CO2-based amphiphilic polycarbonate micelles enable a reliable and efficient platform for tumor imaging. Theranostics. 7(19). 4689–4698. 25 indexed citations
19.
Liu, Shunjie, et al.. (2017). Construction of Well-Defined Redox-Responsive CO2-Based Polycarbonates: Combination of Immortal Copolymerization and Prereaction Approach. Macromolecular Rapid Communications. 38(9). 1600754–1600754. 21 indexed citations
20.
Zhao, Xun, Yanhong Wu, Weiwei Cui, et al.. (2017). SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics. 24(1). 246–255. 35 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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