Xi Wu

1.6k total citations · 1 hit paper
81 papers, 974 citations indexed

About

Xi Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Xi Wu has authored 81 papers receiving a total of 974 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computer Vision and Pattern Recognition, 24 papers in Artificial Intelligence and 18 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Xi Wu's work include Medical Image Segmentation Techniques (12 papers), Advanced Neural Network Applications (12 papers) and Advanced Neuroimaging Techniques and Applications (12 papers). Xi Wu is often cited by papers focused on Medical Image Segmentation Techniques (12 papers), Advanced Neural Network Applications (12 papers) and Advanced Neuroimaging Techniques and Applications (12 papers). Xi Wu collaborates with scholars based in China, United States and Canada. Xi Wu's co-authors include Xinyu Song, Zhengjie Gao, Ao Feng, Jiliu Zhou, Zhaohua Ding, John C. Gore, Dong Nie, Yan Wang, Zhipeng Yang and Jiliu Zhou and has published in prestigious journals such as NeuroImage, IEEE Access and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Xi Wu

74 papers receiving 934 citations

Hit Papers

Target-Dependent Sentiment Classification With BERT 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Target-Dependent Sentiment Classification With BERT
    2019 295 citations Xi Wu et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xi Wu China 16 388 268 244 112 65 81 974
Steven A. Hicks Norway 18 476 1.2× 314 1.2× 355 1.5× 85 0.8× 104 1.6× 67 1.5k
Bernardino Romera‐Paredes United Kingdom 14 326 0.8× 241 0.9× 106 0.4× 87 0.8× 73 1.1× 18 1.0k
Muhammed Yıldırım Türkiye 18 448 1.2× 413 1.5× 411 1.7× 35 0.3× 125 1.9× 86 1.2k
Farhan Riaz Pakistan 16 336 0.9× 317 1.2× 127 0.5× 267 2.4× 120 1.8× 74 1.2k
Melissa Berthelot United Kingdom 6 511 1.3× 203 0.8× 346 1.4× 98 0.9× 212 3.3× 11 1.3k
Shih-Cheng Huang United States 10 535 1.4× 184 0.7× 589 2.4× 34 0.3× 140 2.2× 22 1.3k
Jonathan Passerat‐Palmbach United Kingdom 10 365 0.9× 233 0.9× 256 1.0× 64 0.6× 79 1.2× 18 773
Yujia Zhou China 15 300 0.8× 315 1.2× 214 0.9× 22 0.2× 196 3.0× 62 1.0k
Devvi Sarwinda Indonesia 14 330 0.9× 227 0.8× 280 1.1× 14 0.1× 57 0.9× 80 854
Veronika Cheplygina Netherlands 14 593 1.5× 464 1.7× 381 1.6× 48 0.4× 111 1.7× 24 1.3k

Countries citing papers authored by Xi Wu

Since Specialization
Citations

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

Fields of papers citing papers by Xi Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xi Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Xi Wu. A scholar is included among the top collaborators of Xi Wu 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 Xi Wu. Xi Wu 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
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Cui, Jiaqi, et al.. (2025). HMT: A Hybrid Multimodal Transformer With Multitask Learning for Survival Prediction in Head and Neck Cancer. IEEE Transactions on Radiation and Plasma Medical Sciences. 9(7). 879–889. 1 indexed citations
4.
Hu, Shu, Bin Zhu, Jinrong Hu, et al.. (2024). Few-Shot Learning for Misinformation Detection Based on Contrastive Models. Electronics. 13(4). 799–799. 3 indexed citations
5.
Zeng, Xinyi, et al.. (2023). Semi-supervised medical image segmentation via hard positives oriented contrastive learning. Pattern Recognition. 146. 110020–110020. 27 indexed citations
6.
Hu, Jing, Xin Wang, Shu Hu, et al.. (2023). Controlling Neural Style Transfer with Deep Reinforcement Learning. 100–108. 1 indexed citations
7.
Hu, Jing, Xin Wang, Shu Hu, et al.. (2023). Attention Guided Policy Optimization for 3D Medical Image Registration. IEEE Access. 11. 65546–65558. 6 indexed citations
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Wu, Xi, et al.. (2023). Deep Learning for Near-Surface Air Temperature Estimation From FengYun 4A Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 13108–13119. 3 indexed citations
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Shi, Canghong, et al.. (2023). CAGAN: Classifier‐augmented generative adversarial networks for weakly‐supervised COVID‐19 lung lesion localisation. IET Computer Vision. 18(1). 1–14. 2 indexed citations
11.
Zeng, Yuan, et al.. (2022). Multi-CAM: A Class Activation Mapping Method Based on Multi-scale Feature Fusion. 37. 294–298. 1 indexed citations
12.
Yang, Zhipeng, Peiyu He, Jiliu Zhou, Zhaohua Ding, & Xi Wu. (2018). Functional Informed Fiber Tracking Using Combination of Diffusion and Functional MRI. IEEE Transactions on Biomedical Engineering. 66(3). 794–801. 3 indexed citations
13.
Li, Min, Kun Zhang, Zhifan Gao, et al.. (2018). Deep Learning intra-image and inter-images features for Co-saliency detection.. British Machine Vision Conference. 291. 16 indexed citations
14.
Liu, Chang, Xi Wu, Xi Yu, et al.. (2018). Fusing multi-scale information in convolution network for MR image super-resolution reconstruction. BioMedical Engineering OnLine. 17(1). 114–114. 29 indexed citations
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Wu, Xi, et al.. (2017). Recognition of Damage Buildings in Hollow Village Based on Texture Feature of Gray Level Co-occurrence Matrix. Bulletin of Surveying and Mapping. 90. 1 indexed citations
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Peng, Jing, et al.. (2016). Dual-domain denoising in three dimensional magnetic resonance imaging. Experimental and Therapeutic Medicine. 12(2). 653–660. 2 indexed citations
17.
Wu, Xi, Shujuan Liu, Min Wu, et al.. (2013). Nonlocal denoising using anisotropic structure tensor for 3D MRI. Medical Physics. 40(10). 101904–101904. 15 indexed citations
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Wu, Xi, et al.. (2012). Globally optimized fiber tracking and hierarchical clustering — a unified framework. Magnetic Resonance Imaging. 30(4). 485–495. 11 indexed citations
19.
Howard, Andrea A., Donald R. Hoover, Kathryn Anastos, et al.. (2010). The Effects of Opiate Use and Hepatitis C Virus Infection on Risk of Diabetes Mellitus in the Women's Interagency HIV Study. JAIDS Journal of Acquired Immune Deficiency Syndromes. 54(2). 152–159. 26 indexed citations
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Lazar, Jason, Xi Wu, Qiuhu Shi, et al.. (2009). Arterial Wave Reflection in HIV-Infected and HIV-Uninfected Rwandan Women. AIDS Research and Human Retroviruses. 25(9). 877–882. 22 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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