Qiaoliang Li

1.4k total citations · 1 hit paper
49 papers, 985 citations indexed

About

Qiaoliang Li is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Qiaoliang Li has authored 49 papers receiving a total of 985 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 11 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Biomedical Engineering. Recurrent topics in Qiaoliang Li's work include Retinal Imaging and Analysis (6 papers), Digital Imaging for Blood Diseases (5 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Qiaoliang Li is often cited by papers focused on Retinal Imaging and Analysis (6 papers), Digital Imaging for Blood Diseases (5 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Qiaoliang Li collaborates with scholars based in China, Hong Kong and Singapore. Qiaoliang Li's co-authors include Tianfu Wang, Huisheng Zhang, Ping Liang, Bowei Feng, Suwen Qi, Depeng Xu, Huisheng Zhang, Zhewei Chen, Bingsheng Huang and Yufeng Ye and has published in prestigious journals such as ACS Applied Materials & Interfaces, International Journal of Hydrogen Energy and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Qiaoliang Li

49 papers receiving 958 citations

Hit Papers

A Cross-Modality Learning... 2015 2026 2018 2022 2015 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qiaoliang Li China 14 599 448 329 132 99 49 985
Song Guo China 15 660 1.1× 465 1.0× 445 1.4× 38 0.3× 92 0.9× 53 1.1k
Neil Joshi United States 13 886 1.5× 230 0.5× 668 2.0× 102 0.8× 197 2.0× 24 1.2k
Adrián Colomer Spain 16 440 0.7× 293 0.7× 226 0.7× 100 0.8× 323 3.3× 56 796
Bryan M. Williams United Kingdom 14 577 1.0× 354 0.8× 410 1.2× 224 1.7× 112 1.1× 63 1.1k
Rahele Kafieh Iran 18 1.0k 1.7× 306 0.7× 586 1.8× 555 4.2× 133 1.3× 87 1.4k
Debdoot Sheet India 16 751 1.3× 650 1.5× 343 1.0× 371 2.8× 329 3.3× 80 1.4k
P.E. Undrill United Kingdom 14 329 0.5× 247 0.6× 153 0.5× 92 0.7× 117 1.2× 35 697
Dahong Qian China 21 664 1.1× 259 0.6× 161 0.5× 265 2.0× 221 2.2× 61 1.3k
Tahir Mahmood South Korea 13 342 0.6× 192 0.4× 88 0.3× 77 0.6× 232 2.3× 30 620
Sri Phani Krishna Karri India 12 724 1.2× 291 0.6× 457 1.4× 404 3.1× 123 1.2× 35 1.0k

Countries citing papers authored by Qiaoliang Li

Since Specialization
Citations

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

Fields of papers citing papers by Qiaoliang Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qiaoliang Li

This figure shows the co-authorship network connecting the top 25 collaborators of Qiaoliang Li. A scholar is included among the top collaborators of Qiaoliang Li 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 Qiaoliang Li. Qiaoliang Li 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.
Luo, Yi, Hangli Gong, Han Luo, et al.. (2025). Creep properties and a time-dependent damage constitutive model of sandstone considering the effect of initial damage. Archives of Civil and Mechanical Engineering. 25(2). 1 indexed citations
2.
Zhang, Xianqi, et al.. (2024). Characterization and constitutive modeling of the high strain rate behavior of granite at low temperatures. Mechanics of Time-Dependent Materials. 28(4). 2681–2704. 3 indexed citations
3.
Li, Qiaoliang, Zhigang Yu, Qi Tao, et al.. (2020). Inspection of visible components in urine based on deep learning. Medical Physics. 47(7). 2937–2949. 10 indexed citations
4.
Qi, Suwen, et al.. (2019). Novel Biochemical Insights in the Cerebrospinal Fluid of Patients with Neurosyphilis Based on a Metabonomics Study. Journal of Molecular Neuroscience. 69(1). 39–48. 16 indexed citations
5.
Li, Qiaoliang, et al.. (2019). A Recognition Method of Urine Cast Based On Deep Learning. 157–161. 5 indexed citations
6.
Li, Qiaoliang, Zhewei Chen, Dexiang Liu, et al.. (2018). Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network. BioMed Research International. 2018. 1–7. 44 indexed citations
7.
Huang, Bin, Zhewei Chen, P.M. Wu, et al.. (2018). Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study. Contrast Media & Molecular Imaging. 2018. 1–12. 61 indexed citations
8.
Li, Qiaoliang, Shi Zhong, Zhewei Chen, et al.. (2017). A high-speed end-to-end approach for retinal arteriovenous segmentation. 1–5. 3 indexed citations
9.
Li, Yishan, Nianbing Zhong, Qiang Liao, et al.. (2017). A biomaterial doped with LaB 6 nanoparticles as photothermal media for enhancing biofilm growth and hydrogen production in photosynthetic bacteria. International Journal of Hydrogen Energy. 42(9). 5793–5803. 21 indexed citations
10.
Li, Qiaoliang, Guangyao Yang, Zhewei Chen, et al.. (2017). Colorectal polyp segmentation using a fully convolutional neural network. 1–5. 58 indexed citations
11.
Zhang, Huisheng, et al.. (2016). Determination of C-Reactive Protein Concentration in Serum Based on Chemiluminescence Analysis. 214–217. 3 indexed citations
12.
Li, Qiaoliang, et al.. (2015). Retinal vessel landmark detection using deep learning and hessian matrix. 387–392. 7 indexed citations
14.
Li, Qiaoliang, et al.. (2015). A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images. IEEE Transactions on Medical Imaging. 35(1). 109–118. 423 indexed citations breakdown →
15.
Li, Qiaoliang, Huisheng Zhang, & Tianfu Wang. (2013). SCALE INVARIANT FEATURE MATCHING USING ROTATION-INVARIANT DISTANCE FOR REMOTE SENSING IMAGE REGISTRATION. International Journal of Pattern Recognition and Artificial Intelligence. 27(2). 1354004–1354004. 6 indexed citations
16.
Li, Qiaoliang, Huisheng Zhang, Yin Li, et al.. (2012). Automatic human spermatozoa detection in microscopic video streams based on OpenCV. 224–227. 14 indexed citations
17.
Li, Qiaoliang, et al.. (2010). Notice of Violation of IEEE Publication Principles - Two-level Tardos fingerprinting code. 2007 85. V3–353. 1 indexed citations
18.
Li, Qiaoliang. (2008). New electronic cash system with higher security and efficiency. Jisuanji yingyong yanjiu. 2 indexed citations
19.
Li, Qiaoliang. (2006). A Review of Infrared Weak and Small Targets Detection under Complicated Background. Infrared Technology. 7 indexed citations
20.
Li, Qiaoliang. (2006). Design of Speed Sensor-Less Vector Control System of ACIM. 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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