Jun Shi

5.0k total citations
188 papers, 3.6k citations indexed

About

Jun Shi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Jun Shi has authored 188 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Radiology, Nuclear Medicine and Imaging, 61 papers in Artificial Intelligence and 52 papers in Biomedical Engineering. Recurrent topics in Jun Shi's work include AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and Muscle activation and electromyography studies (20 papers). Jun Shi is often cited by papers focused on AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and Muscle activation and electromyography studies (20 papers). Jun Shi collaborates with scholars based in China, Hong Kong and United States. Jun Shi's co-authors include Shihui Ying, Qi Zhang, Yong‐Ping Zheng, Zheng Xiao, Jun Wang, Qi Zhang, Yan Li, Shichong Zhou, Hairong Zheng and Yang Xiao and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and IEEE Access.

In The Last Decade

Jun Shi

178 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Shi China 31 1.4k 1.2k 807 779 425 188 3.6k
Chunfeng Lian United States 31 867 0.6× 975 0.8× 461 0.6× 752 1.0× 397 0.9× 96 2.9k
Ehsan Adeli United States 36 1.6k 1.1× 1.2k 1.0× 432 0.5× 1.6k 2.0× 533 1.3× 139 4.6k
Defeng Wang Hong Kong 32 577 0.4× 966 0.8× 539 0.7× 789 1.0× 511 1.2× 178 3.9k
Christian Desrosiers Canada 25 748 0.5× 1.1k 0.9× 349 0.4× 991 1.3× 250 0.6× 144 2.7k
M. Iqbal Saripan Malaysia 30 712 0.5× 960 0.8× 381 0.5× 1.2k 1.5× 160 0.4× 176 3.2k
U. Raghavendra India 31 586 0.4× 1.2k 1.0× 366 0.5× 854 1.1× 500 1.2× 84 3.1k
Şengül Doğan Türkiye 39 943 0.7× 633 0.5× 524 0.6× 887 1.1× 1.6k 3.9× 235 4.6k
Dong Nie United States 31 1.0k 0.7× 1.9k 1.6× 778 1.0× 1.7k 2.2× 168 0.4× 85 3.9k
Greg Slabaugh United Kingdom 30 1.2k 0.8× 1.2k 1.0× 640 0.8× 1.9k 2.4× 287 0.7× 145 4.4k

Countries citing papers authored by Jun Shi

Since Specialization
Citations

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

Fields of papers citing papers by Jun Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Shi. A scholar is included among the top collaborators of Jun Shi 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 Jun Shi. Jun Shi 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
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Feng, Yifan, et al.. (2025). Hypergraph Foundation Model for Brain Disease Diagnosis. IEEE Transactions on Neural Networks and Learning Systems. 36(10). 17702–17716. 6 indexed citations
3.
Guo, Le‐Hang, et al.. (2024). Multi-View disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images. Information Processing & Management. 61(6). 103855–103855. 1 indexed citations
4.
Zhang, Hao, Qi Wang, Jun Shi, Shihui Ying, & Zhijie Wen. (2024). Deep unfolding network with spatial alignment for multi-modal MRI reconstruction. Medical Image Analysis. 99. 103331–103331. 4 indexed citations
5.
Li, Juncheng, et al.. (2024). EWT: Efficient Wavelet-Transformer for single image denoising. Neural Networks. 177. 106378–106378. 20 indexed citations
6.
Shi, Jun, et al.. (2024). A Novel Open Set Adaptation Network for Marine Machinery Fault Diagnosis. Journal of Marine Science and Engineering. 12(8). 1382–1382. 1 indexed citations
7.
Li, Jianping, Huihui Fan, Kang Zhou, et al.. (2024). Efficacy and safety of avatrombopag in combination with standard immunosuppressive therapy for severe aplastic anemia. Experimental Hematology. 140. 104670–104670. 1 indexed citations
8.
Zhao, Yang, et al.. (2024). Few sampling meshes-based 3D tooth segmentation via region-aware graph convolutional network. Expert Systems with Applications. 252. 124255–124255. 5 indexed citations
9.
Li, Juncheng, et al.. (2024). WeaFU: Weather-Informed Image Blind Restoration via Multi-Weather Distribution Diffusion. IEEE Transactions on Circuits and Systems for Video Technology. 34(12). 13530–13542. 2 indexed citations
10.
Qiao, Liang, Shichong Zhou, Jun Wang, et al.. (2024). Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images. IEEE Transactions on Medical Imaging. 43(7). 2509–2521. 16 indexed citations
11.
Wang, Xin, Jun Wang, Fei Shan, et al.. (2023). Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning. Computers in Biology and Medicine. 159. 106890–106890. 3 indexed citations
12.
Zhang, Bohan, Yaxin Chen, Wenjuan Wang, et al.. (2022). Type II collagen facilitates gouty arthritis by regulating MSU crystallisation and inflammatory cell recruitment. Annals of the Rheumatic Diseases. 82(3). 416–427. 37 indexed citations
13.
Shen, Lu, Qianting Wang, & Jun Shi. (2020). [Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information].. PubMed Central. 37(3). 405–411. 4 indexed citations
14.
Zhang, Qi, et al.. (2018). Artificial Intelligence Based Diagnosis for Cervical Lymph Node Malignancy Using the Point-Wise Gated Boltzmann Machine. IEEE Access. 6. 60605–60612. 8 indexed citations
15.
Ying, Shihui, Zhijie Wen, Jun Shi, et al.. (2017). Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning. IEEE Transactions on Neural Networks and Learning Systems. 1–12. 58 indexed citations
16.
Zhang, Qi, Jing Yao, Yehua Cai, et al.. (2017). Elevated hardness of peripheral gland on real-time elastography is an independent marker for high-risk prostate cancers. La radiologia medica. 122(12). 944–951. 3 indexed citations
17.
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Zhang, Qi, et al.. (2016). Sonoelastography shows that Achilles tendons with insertional tendinopathy are harder than asymptomatic tendons. Knee Surgery Sports Traumatology Arthroscopy. 25(6). 1839–1848. 13 indexed citations
19.
Zhu, Jie, Jun Shi, Zhiqiang Li, & Xin Chen. (2014). Co-training based semi-supervised classification of Alzheimer's disease. 729–732. 7 indexed citations
20.
Zhang, D., et al.. (2004). Optimal design for cogging torque reduction of transverse flux permanent motor using particle swarm optimization algorithm. International Power Electronics and Motion Control Conference. 1. 260–263. 14 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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