Shengzhou Xu

1.0k total citations · 1 hit paper
22 papers, 699 citations indexed

About

Shengzhou Xu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Shengzhou Xu has authored 22 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Shengzhou Xu's work include AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Medical Image Segmentation Techniques (8 papers). Shengzhou Xu is often cited by papers focused on AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Medical Image Segmentation Techniques (8 papers). Shengzhou Xu collaborates with scholars based in China, United States and South Korea. Shengzhou Xu's co-authors include Enmin Song, Lianghai Jin, Xiangyang Xu, Hong Liu, Jun Tie, Xiangyang Xu, Lu Huang, Chih‐Cheng Hung, Liman Liu and Haihua Liu and has published in prestigious journals such as PLoS ONE, IEEE Access and Pattern Recognition Letters.

In The Last Decade

Shengzhou Xu

17 papers receiving 668 citations

Hit Papers

Characteristic analysis of Otsu threshold and its applica... 2011 2026 2016 2021 2011 100 200 300 400
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. Characteristic analysis of Otsu threshold and its applications
    2011 439 citations Xiangyang Xu, Shengzhou Xu et al. Pattern Recognition Letters profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shengzhou Xu China 9 286 175 168 92 70 22 699
Maxim Berman Belgium 4 425 1.5× 172 1.0× 152 0.9× 88 1.0× 84 1.2× 6 847
Ping‐Sung Liao Taiwan 4 374 1.3× 98 0.6× 82 0.5× 112 1.2× 66 0.9× 8 732
Shoubhik Debnath United States 5 320 1.1× 147 0.8× 70 0.4× 80 0.9× 47 0.7× 9 699
Isao Horiba Japan 13 470 1.6× 226 1.3× 296 1.8× 92 1.0× 129 1.8× 43 922
Xinlei Chen China 11 639 2.2× 377 2.2× 152 0.9× 89 1.0× 54 0.8× 24 1.2k
Ibrahem Kandel Portugal 9 201 0.7× 204 1.2× 216 1.3× 34 0.4× 79 1.1× 9 710
Zhuang Liu China 7 278 1.0× 183 1.0× 68 0.4× 71 0.8× 36 0.5× 21 673
P. Ganesan India 16 352 1.2× 89 0.5× 85 0.5× 180 2.0× 59 0.8× 69 856
Jiayu Xu China 9 408 1.4× 256 1.5× 223 1.3× 72 0.8× 110 1.6× 27 738
Jun Hao Liew Singapore 9 799 2.8× 431 2.5× 137 0.8× 136 1.5× 72 1.0× 12 1.1k

Countries citing papers authored by Shengzhou Xu

Since Specialization
Citations

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

Fields of papers citing papers by Shengzhou Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shengzhou Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Shengzhou Xu. A scholar is included among the top collaborators of Shengzhou Xu 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 Shengzhou Xu. Shengzhou Xu 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.
Yao, Wei, et al.. (2025). Fine-Grained Style Alignment and Class Balance for Unsupervised Domain Adaptation in Remote Sensing Image Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 16663–16679.
2.
Zhang, Wenqian, et al.. (2025). AE‐YOLO: Feature Focus Enhancement for Breast Mass Detection. International Journal of Imaging Systems and Technology. 35(5).
3.
Min, Xiangde, et al.. (2024). Detection of oesophageal cancer based on the JS-DETR model. Biomedical Signal Processing and Control. 102. 107230–107230.
4.
Xu, Shengzhou, et al.. (2024). ERetinaNet: An Efficient Neural Network Based on RetinaNet for Mammographic Breast Mass Detection. IEEE Journal of Biomedical and Health Informatics. 28(5). 2866–2878. 8 indexed citations
5.
Xu, Shengzhou, et al.. (2023). CA‐UNet: Convolution and attention fusion for lung nodule segmentation. International Journal of Imaging Systems and Technology. 33(5). 1469–1479. 4 indexed citations
6.
Tie, Jun, et al.. (2022). Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation. Frontiers in Neuroinformatics. 16. 911679–911679. 62 indexed citations
7.
Zhang, Peipei, Junfeng Gao, Zhaoyan Feng, et al.. (2022). Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram. Frontiers in Oncology. 12. 766243–766243. 11 indexed citations
8.
Xu, Shengzhou, et al.. (2022). Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet. International Journal of Biomedical Imaging. 2022. 1–10. 1 indexed citations
9.
Xu, Shengzhou, et al.. (2022). Mammographic mass recognition using feature reuse and channel attention mechanism. International Journal of Imaging Systems and Technology. 32(6). 2154–2162.
10.
Xu, Shengzhou, et al.. (2020). Left Ventricle Segmentation Based on a Dilated Dense Convolutional Networks. IEEE Access. 8. 214087–214097. 5 indexed citations
11.
Xu, Shengzhou, Ehsan Adeli, Jie‐Zhi Cheng, et al.. (2020). Mammographic mass segmentation using multichannel and multiscale fully convolutional networks. International Journal of Imaging Systems and Technology. 30(4). 1095–1107. 13 indexed citations
12.
Gao, Zhiyong, Liman Liu, Haihua Liu, et al.. (2014). Automatic Segmentation of the Left Ventricle in Cardiac MRI Using Local Binary Fitting Model and Dynamic Programming Techniques. PLoS ONE. 9(12). e114760–e114760. 30 indexed citations
13.
Xu, Shengzhou, Hong Liu, & Enmin Song. (2011). Marker-Controlled Watershed for Lesion Segmentation in Mammograms. Journal of Digital Imaging. 24(5). 754–763. 64 indexed citations
14.
Xu, Shengzhou, et al.. (2011). Hierarchical matching for automatic detection of masses in mammograms. 4523–4526. 7 indexed citations
15.
Xu, Xiangyang, Shengzhou Xu, Lianghai Jin, & Enmin Song. (2011). Characteristic analysis of Otsu threshold and its applications. Pattern Recognition Letters. 32(7). 956–961. 439 indexed citations breakdown →
16.
Song, Enmin, et al.. (2010). Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming. Academic Radiology. 17(11). 1414–1424. 25 indexed citations
17.
Xu, Xiangyang, et al.. (2010). Using PSO to improve dynamic programming based algorithm for breast mass segmentation. 485–488. 4 indexed citations
18.
Xu, Shengzhou, et al.. (2010). Segmentation of the breast region in mammograms using marker-controlled watershed transform. 2371–2374. 12 indexed citations
19.
Xu, Shengzhou. (2009). Non-rigid Mammogram Registration Based on Improved Demons Algorithm. 1 indexed citations
20.
Xu, Zhengguang, Baojun Huang, & Shengzhou Xu. (2008). Exact location of extrema for empirical mode decomposition. Electronics Letters. 44(8). 551–552. 4 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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