Zeju Li

1.6k total citations
24 papers, 684 citations indexed

About

Zeju Li is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Genetics. According to data from OpenAlex, Zeju Li has authored 24 papers receiving a total of 684 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Genetics. Recurrent topics in Zeju Li's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Glioma Diagnosis and Treatment (6 papers) and Medical Image Segmentation Techniques (5 papers). Zeju Li is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Glioma Diagnosis and Treatment (6 papers) and Medical Image Segmentation Techniques (5 papers). Zeju Li collaborates with scholars based in China, United Kingdom and Germany. Zeju Li's co-authors include Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Cao, Zhifeng Shi, Ying Mao, Yuan Gao, Tongtong Liu, Liang Chen and Guoqing Wu and has published in prestigious journals such as Nature Methods, Scientific Reports and IEEE Access.

In The Last Decade

Zeju Li

24 papers receiving 673 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zeju Li China 12 489 232 155 131 125 24 684
Yusong Lin China 18 594 1.2× 156 0.7× 163 1.1× 124 0.9× 125 1.0× 56 929
Michelle Bardis United States 8 370 0.8× 178 0.8× 132 0.9× 92 0.7× 151 1.2× 12 576
Ujjwal Baid India 11 301 0.6× 96 0.4× 127 0.8× 150 1.1× 112 0.9× 23 494
Mohamed Shehata Egypt 14 377 0.8× 57 0.2× 152 1.0× 122 0.9× 142 1.1× 55 648
Zekun Jiang China 15 433 0.9× 57 0.2× 158 1.0× 101 0.8× 159 1.3× 65 732
Garth M. Beache United States 13 514 1.1× 114 0.5× 107 0.7× 196 1.5× 188 1.5× 49 1.1k
Xuxin Chen United States 11 400 0.8× 42 0.2× 307 2.0× 157 1.2× 129 1.0× 40 858
Yulong Yan United States 19 547 1.1× 127 0.5× 81 0.5× 79 0.6× 546 4.4× 88 1.1k
Guanzhong Gong China 15 470 1.0× 39 0.2× 117 0.8× 156 1.2× 239 1.9× 91 753

Countries citing papers authored by Zeju Li

Since Specialization
Citations

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

Fields of papers citing papers by Zeju Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zeju Li

This figure shows the co-authorship network connecting the top 25 collaborators of Zeju Li. A scholar is included among the top collaborators of Zeju 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 Zeju Li. Zeju 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.
Li, Wenjie, Zeju Li, Boxue Tian, et al.. (2024). EvoAI enables extreme compression and reconstruction of the protein sequence space. Nature Methods. 22(1). 102–112. 4 indexed citations
2.
Li, Zeju, Chao Zhang, Xiaoyan Wang, et al.. (2024). 3DMIT: 3D Multi-Modal Instruction Tuning for Scene Understanding. 1–5. 9 indexed citations
3.
Liu, Bo, et al.. (2023). W-AMA: Weight-aware Approximate Multiplication Architecture for neural processing. Computers & Electrical Engineering. 111. 108921–108921. 1 indexed citations
4.
Gong, Yuan, et al.. (2023). Metabolic Profile of Alzheimer’s Disease: Is 10-Hydroxy-2-decenoic Acid a Pertinent Metabolic Adjuster?. Metabolites. 13(8). 954–954. 4 indexed citations
5.
Li, Zeju, Xinghan Liu, Guoshun Nan, et al.. (2023). Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications. 5614–5619. 7 indexed citations
6.
Li, Zeju, Konstantinos Kamnitsas, Qi Dou, Chen Qin, & Ben Glocker. (2023). Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation. IEEE Transactions on Medical Imaging. 42(11). 3323–3335. 4 indexed citations
7.
Li, Zeju, Konstantinos Kamnitsas, Cheng Ouyang, Chen Chen, & Ben Glocker. (2023). Context Label Learning: Improving Background Class Representations in Semantic Segmentation. IEEE Transactions on Medical Imaging. 42(6). 1885–1896. 11 indexed citations
8.
Chen, Chen, Chen Qin, Cheng Ouyang, et al.. (2022). Enhancing MR image segmentation with realistic adversarial data augmentation. Medical Image Analysis. 82. 102597–102597. 29 indexed citations
9.
Chen, Simin, et al.. (2022). Design of intelligent blind guide device based on image learning and positioning. 26. 72–72. 1 indexed citations
10.
Li, Zeju, Jinfei Zhou, Guoshun Nan, et al.. (2022). SemBAT: Physical Layer Black-box Adversarial Attacks for Deep Learning-based Semantic Communication Systems. 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). 1–5. 8 indexed citations
11.
Wang, Yuanyuan, Abudumijiti Aibaidula, Kuangyu Shi, et al.. (2020). A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas. Laboratory Investigation. 101(4). 450–462. 20 indexed citations
12.
Han, Hu, et al.. (2020). High-Resolution Chest X-Ray Bone Suppression Using Unpaired CT Structural Priors. IEEE Transactions on Medical Imaging. 39(10). 3053–3063. 28 indexed citations
13.
Gu, Jiaqi, et al.. (2019). Deep Generative Adversarial Networks for Thin-Section Infant MR Image Reconstruction. IEEE Access. 7. 68290–68304. 13 indexed citations
14.
Li, Zeju, et al.. (2019). DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution. IEEE Transactions on Cybernetics. 51(7). 3441–3454. 23 indexed citations
15.
Wu, Guoqing, Zeju Li, Yuanyuan Wang, et al.. (2018). [Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system].. PubMed. 35(5). 754–760. 1 indexed citations
16.
Li, Zeju, Yuanyuan Wang, Jinhua Yu, Yi Guo, & Wei Cao. (2017). Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Scientific Reports. 7(1). 5467–5467. 246 indexed citations
17.
Li, Zeju, Yuanyuan Wang, Jinhua Yu, Yi Guo, & Qi Zhang. (2017). Age groups related glioblastoma study based on radiomics approach. PubMed. 22(sup1). 18–25. 4 indexed citations
18.
Chen, Yinsheng, Zeju Li, Guoqing Wu, et al.. (2017). Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. International Journal of Neuroscience. 128(7). 608–618. 19 indexed citations
19.
Li, Zeju, Yuanyuan Wang, Jinhua Yu, et al.. (2017). Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF. Journal of Healthcare Engineering. 2017. 1–12. 27 indexed citations
20.
Yu, Jinhua, Zhifeng Shi, Zeju Li, et al.. (2016). Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. European Radiology. 27(8). 3509–3522. 183 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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