Jin Liu

2.5k total citations
89 papers, 1.9k citations indexed

About

Jin Liu is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jin Liu has authored 89 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Biomedical Engineering, 35 papers in Radiology, Nuclear Medicine and Imaging and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jin Liu's work include Medical Imaging Techniques and Applications (30 papers), Advanced X-ray and CT Imaging (29 papers) and Radiation Dose and Imaging (21 papers). Jin Liu is often cited by papers focused on Medical Imaging Techniques and Applications (30 papers), Advanced X-ray and CT Imaging (29 papers) and Radiation Dose and Imaging (21 papers). Jin Liu collaborates with scholars based in China, United States and France. Jin Liu's co-authors include Yang Chen, Chunzhong Li, Ling Zhang, Jian Yang, Guotao Quan, Qianlong Zhao, Dianlin Hu, Gouenou Coatrieux, Limin Luo and Yikun Zhang and has published in prestigious journals such as Journal of the American Chemical Society, Environmental Science & Technology and The Science of The Total Environment.

In The Last Decade

Jin Liu

81 papers receiving 1.9k citations

Peers

Jin Liu

RNMI

CVPR

EEE

Yutong Liu China

Chang Peng China

Sanghyun Woo South Korea

Lianghao Han United Kingdom

Sabrina Grassini Italy

Haiying Zhang China

Lin Qi China

Manish Arora India

Mustafa Karaman Türkiye

Jianqi Wang China

Yutong Liu China

Jin Liu

205 ×0.2

183 ×0.2

RNMI

107 ×0.3

CVPR

52 ×0.3

EEE

24 ×0.2

Citations per year, relative to Jin Liu Jin Liu (= 1×) peers Yutong Liu

Countries citing papers authored by Jin Liu

Since Specialization

Citations

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

Fields of papers citing papers by Jin Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jin Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Jin Liu. A scholar is included among the top collaborators of Jin Liu 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 Jin Liu. Jin Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Jin, et al.. (2025). DECT sparse reconstruction based on hybrid spectrum data generative diffusion model. Computer Methods and Programs in Biomedicine. 261. 108597–108597.

Liu, Jin, Tong Jin, Kun Wang, et al.. (2025). LWCDNet: An Interpretable Learning Weighted Convolutional Dictionary Network for Metal Artifact Reduction in CT Images. IEEE Transactions on Instrumentation and Measurement. 74. 1–15. 1 indexed citations

Wang, Kun, Yuting Lü, Jin Liu, & Xiaohong Zhang. (2025). GAML: Geometry-Aware Mutual Learning for polyp segmentation. Biomedical Signal Processing and Control. 108. 107965–107965. 1 indexed citations

Yang, Ao, Shirui Sun, Hongfu Mi, et al.. (2024). Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO₂ Solubility in Ionic Liquids. Industrial & Engineering Chemistry Research. 63(18). 8293–8305. 8 indexed citations

Liu, Jin, et al.. (2023). Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging. Quantitative Imaging in Medicine and Surgery. 13(8). 5271–5293. 4 indexed citations

Liu, Jin, T. Zhang, Yong Wang, et al.. (2023). Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging. Biomedical Signal Processing and Control. 85. 104868–104868. 8 indexed citations

Gao, Yuan, Hui Tang, Rongjun Ge, et al.. (2023). 3DSRNet: 3-D Spine Reconstruction Network Using 2-D Orthogonal X-Ray Images Based on Deep Learning. IEEE Transactions on Instrumentation and Measurement. 72. 1–14. 11 indexed citations

Pan, Chao, et al.. (2023). Deep learning network for fusing optical and infrared images in a complex imaging environment by using the modified U-Net. Journal of the Optical Society of America A. 40(9). 1644–1644.

Hu, Dianlin, Yikun Zhang, Jin Liu, et al.. (2022). PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT. IEEE Journal of Biomedical and Health Informatics. 26(11). 5551–5562. 19 indexed citations

10.

Zhang, Yikun, Dianlin Hu, Jin Liu, et al.. (2022). DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction. IEEE Journal of Biomedical and Health Informatics. 27(1). 480–491. 23 indexed citations

11.

Zhang, Yikun, Rongjun Ge, Qianlong Zhao, et al.. (2021). CD-Net: Comprehensive Domain Network With Spectral Complementary for DECT Sparse-View Reconstruction. IEEE Transactions on Computational Imaging. 7. 436–447. 36 indexed citations

12.

Zhang, Yikun, Dianlin Hu, Qianlong Zhao, et al.. (2021). CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging. IEEE Transactions on Medical Imaging. 40(11). 3089–3101. 73 indexed citations

13.

Hu, Dianlin, Yikun Zhang, Jin Liu, et al.. (2021). SPECIAL: Single-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT. IEEE Transactions on Computational Imaging. 7. 734–746. 31 indexed citations

14.

Hao, Lijun, Chunxiao Hao, Liang Ji, et al.. (2021). Statistical Modeling of Exhaust Emissions from Gasoline Passenger Cars. Journal of Beijing Institute of Technology. 30. 52–63. 1 indexed citations

15.

Hu, Dianlin, Jin Liu, Qianlong Zhao, et al.. (2020). Hybrid-Domain Neural Network Processing for Sparse-View CT Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences. 5(1). 88–98. 75 indexed citations

16.

Liu, Jin, Yi Zhang, Qianlong Zhao, et al.. (2019). Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging. Physics in Medicine and Biology. 64(13). 135007–135007. 41 indexed citations

17.

Coatrieux, Jean-Louis, Qianlong Zhao, Jin Liu, et al.. (2019). Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging. IEEE Transactions on Medical Imaging. 38(12). 2903–2913. 172 indexed citations

18.

Hu, Dianlin, Weiwen Wu, Yanbo Zhang, et al.. (2019). SISTER: Spectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT. IEEE Transactions on Computational Imaging. 6. 477–490. 40 indexed citations

19.

Liu, Jin. (2014). Clinical Observations on Yin-yang-balancing Point-to-point Acupuncture for Walking Function Reconstruction in Patients with Post-stroke Spastic Paralysis. Shanghai zhenjiu zazhi. 1 indexed citations

20.

Wang, Lanlan, et al.. (2002). Relationship between disequilibrium of T lymphocyte subgroups and inflammatory adhesion molecules in patients with rheumatoid arthritis. 18(5). 378–380. 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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