Shan Tan

2.4k total citations · 1 hit paper
79 papers, 1.4k citations indexed

About

Shan Tan is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Shan Tan has authored 79 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Computer Vision and Pattern Recognition and 21 papers in Biomedical Engineering. Recurrent topics in Shan Tan's work include Medical Imaging Techniques and Applications (27 papers), Radiomics and Machine Learning in Medical Imaging (20 papers) and Image and Signal Denoising Methods (16 papers). Shan Tan is often cited by papers focused on Medical Imaging Techniques and Applications (27 papers), Radiomics and Machine Learning in Medical Imaging (20 papers) and Image and Signal Denoising Methods (16 papers). Shan Tan collaborates with scholars based in China, United States and Hong Kong. Shan Tan's co-authors include Wei Lü, Laquan Li, Haosen Liu, Liangyi Chen, Xiaoshuai Huang, W DˈSouza, Liuju Li, Junchao Fan, Lisi Wei and Xi Peng and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Nature Methods.

In The Last Decade

Shan Tan

68 papers receiving 1.4k citations

Hit Papers

Fast, long-term, super-resolution imaging with Hessian st... 2018 2026 2020 2023 2018 100 200 300 400
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. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy
    2018 430 citations Xiaoshuai Huang, Junchao Fan et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shan Tan China 16 599 414 338 278 195 79 1.4k
Martin Halicek United States 18 825 1.4× 500 1.2× 312 0.9× 162 0.6× 76 0.4× 43 1.7k
Xin Yang China 29 757 1.3× 1.2k 3.0× 233 0.7× 393 1.4× 123 0.6× 133 2.5k
Pinaki Sarder United States 16 211 0.4× 327 0.8× 258 0.8× 245 0.9× 129 0.7× 81 1.3k
Tingying Peng Germany 15 473 0.8× 219 0.5× 239 0.7× 318 1.1× 65 0.3× 44 1.3k
Mirabela Rusu United States 20 516 0.9× 182 0.4× 144 0.4× 211 0.8× 425 2.2× 84 1.2k
Xiaochao Qu China 22 697 1.2× 758 1.8× 201 0.6× 577 2.1× 113 0.6× 65 1.7k
Frank G. Zöllner Germany 25 1.5k 2.5× 313 0.8× 100 0.3× 375 1.3× 292 1.5× 138 2.2k
Adam M. Zysk United States 17 549 0.9× 1.1k 2.8× 419 1.2× 64 0.2× 133 0.7× 37 1.5k
Banghe Zhu United States 20 255 0.4× 404 1.0× 39 0.1× 508 1.8× 109 0.6× 52 1.3k
Yuanming Feng China 18 273 0.5× 234 0.6× 194 0.6× 85 0.3× 253 1.3× 93 864

Countries citing papers authored by Shan Tan

Since Specialization
Citations

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

Fields of papers citing papers by Shan Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shan Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Shan Tan. A scholar is included among the top collaborators of Shan Tan 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 Shan Tan. Shan Tan 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.
Tan, Shan, Zhen Fan, Haoyue Deng, et al.. (2025). In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages. Nature Communications. 17(1). 408–408.
2.
Li, Wenjie, Shan Tan, Zhen Fan, et al.. (2025). Piezoelectric neuron for neuromorphic computing. Journal of Materiomics. 11(5). 101013–101013. 1 indexed citations
3.
Liu, Jiahao, Xue Dong, Gaowen Liu, et al.. (2025). Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured illumination microscopy. Nature Methods. 23(2). 395–404.
4.
Liu, Tao, Jiahao Liu, Dong Li, & Shan Tan. (2025). Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification. Nature Communications. 16(1). 5027–5027. 3 indexed citations
5.
6.
7.
Fan, Junchao, Xiaoshuai Huang, Liuju Li, Liangyi Chen, & Shan Tan. (2019). One-step deconvolution for multi-angle TIRF microscopy with enhanced resolution. Biomedical Optics Express. 10(3). 1097–1097.
8.
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
9.
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
10.
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
11.
Shi, Qi, Nanbo Sun, Tao Sun, Jing Wang, & Shan Tan. (2016). Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties. Biomedical Optics Express. 7(9). 3299–3299. 8 indexed citations
12.
Tan, Shan, et al.. (2014). Large Volume Osteosarcomas of the Femur Treated with Total Femoral Replacement. Malaysian Orthopaedic Journal. 8(1). 32–36. 9 indexed citations
13.
Tan, Shan, Licheng Jiao, & Ioannis A. Kakadiaris. (2008). Wavelet-Based Bayesian Image Estimation: From Marginal and Bivariate Prior Models to Multivariate Prior Models. IEEE Transactions on Image Processing. 17(4). 469–481. 7 indexed citations
14.
Zhang, Xiangrong, Shuang Wang, Shan Tan, & Licheng Jiao. (2005). Radar target recognition using SVMs with a wrapper feature selection driven by Immune Clonal Algorithm. The European Symposium on Artificial Neural Networks. 577–582. 1 indexed citations
15.
Tan, Shan & Licheng Jiao. (2005). New evidences for sparse coding strategy employed in visual neurons: from the image processing and nonlinear approximation viewpoint. The European Symposium on Artificial Neural Networks. 441–446. 3 indexed citations
16.
Huang, Wentao, Licheng Jiao, Shan Tan, & Maoguo Gong. (2005). Response Analysis of Neuronal Population with Synaptic Depression. Neural Information Processing Systems. 18. 523–530. 1 indexed citations
17.
Tan, Shan. (2005). SAR Speckle Reduction with 2-D Lifting Transform. Radar Science and Technology.
18.
Tan, Shan & Licheng Jiao. (2005). Ridgelet bi-frame. Applied and Computational Harmonic Analysis. 20(3). 391–402. 4 indexed citations
19.
Tan, Shan. (2003). Development and Prospect of Image Multiscale Geometric Analysis. Dianzi xuebao. 34 indexed citations
20.
Watanabe, Yukio, Masahiro Tada, Akane Higashi, et al.. (1986). [A case-control study of diet and colo-rectal cancer in Japan].. PubMed. 32(6). 576–81. 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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