Guangquan Zhou

2.0k total citations
96 papers, 1.3k citations indexed

About

Guangquan Zhou is a scholar working on Biomedical Engineering, Surgery and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Guangquan Zhou has authored 96 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Biomedical Engineering, 28 papers in Surgery and 28 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Guangquan Zhou's work include Ultrasound Imaging and Elastography (15 papers), Scoliosis diagnosis and treatment (12 papers) and AI in cancer detection (11 papers). Guangquan Zhou is often cited by papers focused on Ultrasound Imaging and Elastography (15 papers), Scoliosis diagnosis and treatment (12 papers) and AI in cancer detection (11 papers). Guangquan Zhou collaborates with scholars based in China, Hong Kong and Bangladesh. Guangquan Zhou's co-authors include Yong‐Ping Zheng, James Chung‐Wai Cheung, Weiwei Jiang, Kai‐Ni Wang, Yi Wang, Tianjie Li, Suk‐Tak Chan, Yongjin Zhou, Man Sang Wong and Yang Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Optics Express.

In The Last Decade

Guangquan Zhou

93 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guangquan Zhou China 19 599 506 288 227 179 96 1.3k
Yoshito Otake Japan 27 826 1.4× 993 2.0× 642 2.2× 364 1.6× 112 0.6× 158 2.0k
In Ho Han South Korea 19 354 0.6× 278 0.5× 182 0.6× 123 0.5× 123 0.7× 80 1.1k
Franco Marinozzi Italy 22 300 0.5× 443 0.9× 249 0.9× 53 0.2× 180 1.0× 127 1.5k
Pietro Cerveri Italy 26 661 1.1× 647 1.3× 310 1.1× 276 1.2× 125 0.7× 153 1.9k
Marie‐Christine Ho Ba Tho France 20 398 0.7× 531 1.0× 159 0.6× 89 0.4× 256 1.4× 90 1.2k
G. Beaudoin Canada 13 566 0.9× 599 1.2× 203 0.7× 164 0.7× 170 0.9× 27 1.4k
Jérôme Thevenot Finland 15 514 0.9× 353 0.7× 250 0.9× 116 0.5× 283 1.6× 52 1.4k
Yoshikazu Nakajima Japan 24 896 1.5× 446 0.9× 174 0.6× 224 1.0× 33 0.2× 105 1.8k
S. Laporte France 16 494 0.8× 426 0.8× 134 0.5× 82 0.4× 119 0.7× 49 868
Philippe Büchler Switzerland 26 1.3k 2.2× 436 0.9× 469 1.6× 232 1.0× 137 0.8× 107 2.3k

Countries citing papers authored by Guangquan Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Guangquan Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guangquan Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Guangquan Zhou. A scholar is included among the top collaborators of Guangquan Zhou 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 Guangquan Zhou. Guangquan Zhou 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.
Wang, Kai‐Ni, Jie Hua, Yi Tang, et al.. (2025). Dynamic spectrum-driven hierarchical learning network for polyp segmentation. Medical Image Analysis. 101. 103449–103449.
2.
Sun, Jiarui, Hui Tang, Guangquan Zhou, et al.. (2024). A Multi-group similarity-decoding-based method for deep model ensembling applied in the microcalcification classification on digital mammograms. Biomedical Signal Processing and Control. 91. 105896–105896. 1 indexed citations
3.
Chen, Yang, Guangquan Zhou, Rongjun Ge, et al.. (2024). GFA-Net: Global Feature Aggregation Network Based on Contrastive Learning for Breast Lesion Automated Segmentation in Ultrasound Images. IEEE Transactions on Instrumentation and Measurement. 74. 1–14. 2 indexed citations
4.
Li, Le, et al.. (2024). TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification. IEEE Transactions on Industrial Informatics. 20(11). 12554–12564. 12 indexed citations
5.
Wang, Kai‐Ni, et al.. (2024). SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation. IEEE Journal of Biomedical and Health Informatics. 28(5). 2854–2865. 18 indexed citations
6.
Zhou, Guangquan, et al.. (2024). Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach. Journal of Imaging Informatics in Medicine. 38(2). 850–857. 1 indexed citations
7.
Ma, Chao, et al.. (2023). Tanshinone I attenuates estrogen-deficiency bone loss via inhibiting RANKL-induced MAPK and NF-κB signaling pathways. International Immunopharmacology. 127. 111322–111322. 8 indexed citations
8.
Zhou, Guangquan, Wenbo Zhang, Zhongqing Shi, et al.. (2023). DSANet: Dual-Branch Shape-Aware Network for Echocardiography Segmentation in Apical Views. IEEE Journal of Biomedical and Health Informatics. 27(10). 4804–4815. 14 indexed citations
9.
Zhou, Guangquan, et al.. (2023). TAGNet: A transformer-based axial guided network for bile duct segmentation. Biomedical Signal Processing and Control. 86. 105244–105244. 4 indexed citations
10.
Miao, Juzheng, et al.. (2023). SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging. 43(4). 1347–1364. 15 indexed citations
11.
Feng, Jun, Kai‐Ni Wang, Hao Guo, et al.. (2023). An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease. Diagnostics. 13(23). 3533–3533. 6 indexed citations
12.
Wang, Kai‐Ni, et al.. (2023). DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images. Medical Image Analysis. 87. 102832–102832. 21 indexed citations
13.
Wang, Kai‐Ni, Juzheng Miao, Yang Chen, et al.. (2023). Adaptive Frequency Learning Network With Anti-Aliasing Complex Convolutions for Colon Diseases Subtypes. IEEE Journal of Biomedical and Health Informatics. 27(10). 4816–4827. 3 indexed citations
14.
Zhou, Guangquan, et al.. (2023). Automatic Myotendinous Junction Identification in Ultrasound Images Based on Junction-Based Template Measurements. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31. 851–862. 4 indexed citations
15.
16.
Zhou, Guangquan, Xiaoyi Wang, Kai‐Ni Wang, et al.. (2023). BSMNet: Boundary-salience multi-branch network for intima-media identification in carotid ultrasound images. Computers in Biology and Medicine. 162. 107092–107092. 4 indexed citations
17.
18.
He, Chen, Michael To, Jason Pui Yin Cheung, et al.. (2017). An effective assessment method of spinal flexibility to predict the initial in-orthosis correction on the patients with adolescent idiopathic scoliosis (AIS). PLoS ONE. 12(12). e0190141–e0190141. 28 indexed citations
19.
Cheung, James Chung‐Wai, et al.. (2015). Ultrasound Volume Projection Imaging for Assessment of Scoliosis. IEEE Transactions on Medical Imaging. 34(8). 1760–1768. 89 indexed citations
20.
Zhou, Guangquan, et al.. (1996). DEPENDENCE OF YIELD STRESS ON GRAIN SIZE OF NANOCRYSTALS. Acta Metallurgica Sinica. 32(9). 959–965. 1 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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