Hongming Shan

4.5k total citations · 1 hit paper
106 papers, 2.6k citations indexed

About

Hongming Shan is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Hongming Shan has authored 106 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Radiology, Nuclear Medicine and Imaging, 51 papers in Computer Vision and Pattern Recognition and 43 papers in Biomedical Engineering. Recurrent topics in Hongming Shan's work include Medical Imaging Techniques and Applications (46 papers), Advanced X-ray and CT Imaging (35 papers) and Radiomics and Machine Learning in Medical Imaging (20 papers). Hongming Shan is often cited by papers focused on Medical Imaging Techniques and Applications (46 papers), Advanced X-ray and CT Imaging (35 papers) and Radiomics and Machine Learning in Medical Imaging (20 papers). Hongming Shan collaborates with scholars based in China, United States and Hong Kong. Hongming Shan's co-authors include Ge Wang, Junping Zhang, Zhizhong Huang, Yi Zhang, Mannudeep K. Kalra, Chuang Niu, Liang Wang, Yiwei He, Uwe Krüger and Qing Lyu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Stroke.

In The Last Decade

Hongming Shan

97 papers receiving 2.6k citations

Hit Papers

Competitive performance of a modularized deep neural netw... 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    2019 280 citations Hongming Shan, Ge Wang et al. profile →

Peers

Hongming Shan
Wentao Zhu China
S. Kevin Zhou United States
Zhifan Gao China
Liyue Shen United States
Limin Luo China
Jean-Louis Coatrieux France
Martin Urschler Austria
Timor Kadir United Kingdom
Yi Xin China
Wentao Zhu China
Hongming Shan
Citations per year, relative to Hongming Shan Hongming Shan (= 1×) peers Wentao Zhu

Countries citing papers authored by Hongming Shan

Since Specialization
Citations

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

Fields of papers citing papers by Hongming Shan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongming Shan

This figure shows the co-authorship network connecting the top 25 collaborators of Hongming Shan. A scholar is included among the top collaborators of Hongming Shan 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 Hongming Shan. Hongming Shan 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.
Yang, Ziyuan, Hui Yu, Andrew Beng Jin Teoh, et al.. (2025). Enhancing federated learning through exploring filter-aware relationships and personalizing local structures. Pattern Recognition. 171. 112281–112281. 1 indexed citations
2.
Li, Binbin, et al.. (2025). A method for power battery cell inconsistency fault identification based on relative skewness density ratio outlier factor. International Journal of Green Energy. 23(1). 44–57.
3.
4.
Ye, Jiaxin, et al.. (2025). DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection. 1–5. 3 indexed citations
5.
Wang, James Z., et al.. (2025). Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation. IEEE/CAA Journal of Automatica Sinica. 12(4). 705–718. 1 indexed citations
6.
Wang, Chenhui, et al.. (2024). HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation. IEEE Transactions on Medical Imaging. 43(10). 3570–3583. 13 indexed citations
7.
Lei, Yiming, et al.. (2024). CORE: Learning consistent ordinal representations with convex optimization for image ordinal estimation. Pattern Recognition. 156. 110748–110748. 2 indexed citations
8.
Lu, Zexin, Qi Gao, Tao Wang, et al.. (2024). PrideDiff: Physics-Regularized Generalized Diffusion Model for CT Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences. 9(2). 157–168. 2 indexed citations
9.
Zhang, Yi, Yingyu Chen, Hui Yu, et al.. (2024). UniAda: Domain Unifying and Adapting Network for Generalizable Medical Image Segmentation. IEEE Transactions on Medical Imaging. 44(5). 1988–2001.
10.
Peng, Zhao, et al.. (2023). Weakly supervised learning‐based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement. Medical Physics. 51(2). 1277–1288. 1 indexed citations
11.
Yuan, Yifan, et al.. (2023). DO-FAM: Disentangled Non-Linear Latent Navigation For Facial Attribute Manipulation. 34. 1–5. 1 indexed citations
12.
Guo, Xiaodong, Peng He, Yuanfeng Liu, et al.. (2023). Material decomposition of spectral CT images via attention-based global convolutional generative adversarial network. Nuclear Science and Techniques. 34(3). 9 indexed citations
13.
Li, Bowen, Hui Yu, Maosong Ran, et al.. (2023). Promoting fast MR imaging pipeline by full-stack AI. iScience. 27(1). 108608–108608. 2 indexed citations
14.
Lu, Zexin, Wenjun Xia, Yongqiang Huang, et al.. (2022). M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising. IEEE Transactions on Medical Imaging. 42(3). 850–863. 25 indexed citations
15.
Lyu, Qing, Hongming Shan, Yibin Xie, et al.. (2021). Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network. IEEE Transactions on Medical Imaging. 40(8). 2170–2181. 49 indexed citations
16.
Meng, Xiangxi, Jiangyuan Yu, Lei Zhu, et al.. (2021). Content-Noise Complementary Learning for Medical Image Denoising. IEEE Transactions on Medical Imaging. 41(2). 407–419. 111 indexed citations
17.
Huang, Zhizhong, Junping Zhang, Yi Zhang, & Hongming Shan. (2021). DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising. arXiv (Cornell University). 146 indexed citations
18.
Teng, Yueyang, Hongming Shan, Taohui Xiao, et al.. (2019). Parameter Constrained Transfer Learning for Low Dose PET Image Denoising. arXiv (Cornell University). 2 indexed citations
19.
Peng, Zhao, Xi Fang, Pingkun Yan, et al.. (2019). A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep Multi-Organ Segmentation Algorithms and GPU-accelerated Monte Carlo Dose Computing Code. arXiv (Cornell University). 1 indexed citations
20.
You, Chenyu, Wenxiang Cong, Ge Wang, et al.. (2018). Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising. IEEE Access. 6. 41839–41855. 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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