Laquan Li

690 total citations
24 papers, 366 citations indexed

About

Laquan Li is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Laquan Li has authored 24 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 14 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Artificial Intelligence. Recurrent topics in Laquan Li's work include Radiomics and Machine Learning in Medical Imaging (14 papers), Medical Image Segmentation Techniques (11 papers) and Medical Imaging Techniques and Applications (10 papers). Laquan Li is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), Medical Image Segmentation Techniques (11 papers) and Medical Imaging Techniques and Applications (10 papers). Laquan Li collaborates with scholars based in China, United States and Hong Kong. Laquan Li's co-authors include Shan Tan, Wei Lü, Mingqi Gao, W DˈSouza, Bin Fang, Wookjin Choi, Yihua Tan, Min Kyu Kang, Jian Wang and Yi Wang and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition and Information Sciences.

In The Last Decade

Laquan Li

22 papers receiving 359 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laquan Li China 10 258 140 78 72 67 24 366
Mostafa Ghelich Oghli Iran 9 237 0.9× 97 0.7× 107 1.4× 98 1.4× 39 0.6× 19 386
Jonnison Lima Ferreira Brazil 8 299 1.2× 132 0.9× 81 1.0× 149 2.1× 119 1.8× 13 427
Nooshin Ghavami United Kingdom 5 174 0.7× 212 1.5× 126 1.6× 60 0.8× 70 1.0× 6 353
Bartłomiej W. Papież United Kingdom 12 258 1.0× 249 1.8× 112 1.4× 57 0.8× 44 0.7× 38 458
Qikui Zhu United States 10 140 0.5× 200 1.4× 85 1.1× 103 1.4× 80 1.2× 22 349
Grzegorz Chlebus Germany 7 216 0.8× 111 0.8× 61 0.8× 120 1.7× 47 0.7× 9 321
Jie Tian China 9 145 0.6× 104 0.7× 53 0.7× 41 0.6× 50 0.7× 31 292
Xia Huang China 9 267 1.0× 83 0.6× 66 0.8× 82 1.1× 136 2.0× 15 387
Koen A. J. Eppenhof Netherlands 10 240 0.9× 187 1.3× 105 1.3× 51 0.7× 59 0.9× 11 367
Riqiang Gao United States 11 213 0.8× 97 0.7× 57 0.7× 94 1.3× 115 1.7× 34 340

Countries citing papers authored by Laquan Li

Since Specialization
Citations

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

Fields of papers citing papers by Laquan Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laquan Li

This figure shows the co-authorship network connecting the top 25 collaborators of Laquan Li. A scholar is included among the top collaborators of Laquan 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 Laquan Li. Laquan 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.
Xian, Sidong, et al.. (2024). Mual: enhancing multimodal sentiment analysis with cross-modal attention and difference loss. International Journal of Multimedia Information Retrieval. 13(3). 5 indexed citations
2.
Li, Laquan, et al.. (2024). Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information. Computers in Biology and Medicine. 180. 108980–108980. 1 indexed citations
3.
Shao, Yabin, et al.. (2024). Con-MGSVM: Controllable multi-granularity support vector algorithm for classification and regression. Information Fusion. 117. 102867–102867. 2 indexed citations
4.
Li, Laquan, et al.. (2023). Automatic abdominal segmentation using novel 3D self-adjustable organ aware deep network in CT images. Biomedical Signal Processing and Control. 84. 104691–104691. 8 indexed citations
5.
6.
Chen, Zhiyuan, et al.. (2023). Learning feature fusion via an interpretation method for tumor segmentation on PET/CT. Applied Soft Computing. 148. 110825–110825. 6 indexed citations
7.
Li, Laquan, et al.. (2023). Dual-attention deep fusion network for multi-modal medical image segmentation. 91. 38–38. 1 indexed citations
8.
Li, Laquan, et al.. (2022). Automated multi-modal Transformer network (AMTNet) for 3D medical images segmentation. Physics in Medicine and Biology. 68(2). 25014–25014. 14 indexed citations
9.
Li, Weisheng, et al.. (2022). L2-Norm Scaled Transformer for 3D Head and Neck Primary Tumors Segmentation in PET-CT. 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 30. 1186–1191. 3 indexed citations
10.
Li, Laquan, et al.. (2022). PET/CT Co-Segmentation Based on Hybrid Active Contour Model. 2022 IEEE International Conference on Image Processing (ICIP). 4143–4147.
11.
Huang, Min-xin, et al.. (2020). Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images. Physics in Medicine and Biology. 65(16). 165013–165013. 9 indexed citations
12.
Li, Laquan, Wei Lü, Yihua Tan, & Shan Tan. (2019). Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration. IEEE Transactions on Radiation and Plasma Medical Sciences. 4(1). 37–49. 15 indexed citations
13.
Li, Laquan, et al.. (2019). Deep learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing. 392. 277–295. 89 indexed citations
14.
Fang, Bin, et al.. (2019). Automatic liver tumour segmentation in CT combining FCN and NMF-based deformable model. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 8(5). 468–477. 11 indexed citations
15.
Li, Laquan, et al.. (2018). Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Physics in Medicine and Biology. 64(1). 15011–15011. 115 indexed citations
16.
Tan, Shan, Laquan Li, Wookjin Choi, et al.. (2017). Adaptive region-growing with maximum curvature strategy for tumor segmentation in18F-FDG PET. Physics in Medicine and Biology. 62(13). 5383–5402. 26 indexed citations
17.
Fang, Bin, et al.. (2017). B-Spline based globally optimal segmentation combining low-level and high-level information. Pattern Recognition. 73. 144–157. 11 indexed citations
18.
Fang, Bin, et al.. (2017). A variational approach to liver segmentation using statistics from multiple sources. Physics in Medicine and Biology. 63(2). 25024–25024. 10 indexed citations
19.
Li, Laquan, Jian Wang, Wei Lü, & Shan Tan. (2016). Simultaneous tumor segmentation, image restoration, and blur kernel estimation in PET using multiple regularizations. Computer Vision and Image Understanding. 155. 173–194. 15 indexed citations
20.
Fang, Bin, et al.. (2016). Multi-scale B-spline level set segmentation based on Gaussian kernel equalization. 52. 4319–4323. 6 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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