Hai Shu

553 total citations
22 papers, 258 citations indexed

About

Hai Shu is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Hai Shu has authored 22 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Hai Shu's work include Medical Image Segmentation Techniques (5 papers), Advanced Neural Network Applications (5 papers) and Medical Imaging Techniques and Applications (4 papers). Hai Shu is often cited by papers focused on Medical Image Segmentation Techniques (5 papers), Advanced Neural Network Applications (5 papers) and Medical Imaging Techniques and Applications (4 papers). Hai Shu collaborates with scholars based in United States, China and Singapore. Hai Shu's co-authors include Hongtu Zhu, Xiao Wang, Bin Nan, Liming Zhong, Wei Yang, Tengfei Li, Qianjin Feng, Yuankui Wu, Fan Zhou and Yan Hu and has published in prestigious journals such as Journal of the American Statistical Association, NeuroImage and IEEE Transactions on Medical Imaging.

In The Last Decade

Hai Shu

19 papers receiving 251 citations

Peers

Hai Shu

CVPR

NEURO

RNMI

Sarah Mazhar China

Jonathan Stoeckel United States

Hancan Zhu China

Lingraj Dora India

P. Sriramakrishnan India

Petru-Daniel Tudosiu United Kingdom

Yao Wu China

Shunbo Hu China

Md Ishtyaq Mahmud United States

Yepuganti Karuna India

Sarah Mazhar China

Hai Shu

120 ×0.9

CVPR

66 ×0.6

NEURO

97 ×1.2

220 ×2.8

RNMI

20 ×0.8

Citations per year, relative to Hai Shu Hai Shu (= 1×) peers Sarah Mazhar

Countries citing papers authored by Hai Shu

Since Specialization

Citations

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

Fields of papers citing papers by Hai Shu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hai Shu

This figure shows the co-authorship network connecting the top 25 collaborators of Hai Shu. A scholar is included among the top collaborators of Hai Shu 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 Hai Shu. Hai Shu 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

Kim, Taehyo, et al.. (2025). UKAN-EP: enhancing U-KAN with efficient attention and pyramid aggregation for 3D multi-modal MRI brain tumor segmentation. BMC Medical Imaging. 25(1). 517–517.

Zhang, Zhaoyang, et al.. (2025). Enhancing missing data imputation through combined bipartite graph and complete directed graph. Neurocomputing. 649. 130717–130717.

Zhong, Liming, Hai Shu, Kaiyi Zheng, et al.. (2024). NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images. Medical Image Analysis. 100. 103397–103397. 1 indexed citations

Zhong, Liming, Hai Shu, Yiwen Zhang, et al.. (2023). United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration. Computer Methods and Programs in Biomedicine. 231. 107391–107391. 7 indexed citations

Li, Heng, Haofeng Liu, Huazhu Fu, et al.. (2023). A generic fundus image enhancement network boosted by frequency self-supervised representation learning. Medical Image Analysis. 90. 102945–102945. 13 indexed citations

Zhong, Liming, Hai Shu, Kaiyi Zheng, et al.. (2023). Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model. IEEE Transactions on Medical Imaging. 43(2). 794–806. 13 indexed citations

Li, Haojin, Heng Li, Hai Shu, et al.. (2023). Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data. 1–5. 1 indexed citations

Zhang, Yiwen, Liming Zhong, Hai Shu, et al.. (2022). Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-at-Risk. IEEE Transactions on Artificial Intelligence. 4(5). 1246–1257. 7 indexed citations

Shu, Hai, et al.. (2022). BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation. Lecture notes in computer science. 2021. 3–14. 80 indexed citations

10.

Zhong, Liming, Hai Shu, Yiwen Zhang, et al.. (2022). QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration. Medical Image Analysis. 83. 102692–102692. 4 indexed citations

11.

Shu, Hai, et al.. (2022). mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks. 2022 International Joint Conference on Neural Networks (IJCNN). 26. 1–8. 3 indexed citations

12.

Shu, Hai, et al.. (2022). CDPA: Common and distinctive pattern analysis between high-dimensional datasets. Electronic Journal of Statistics. 16(1). 2475–2517.

13.

Shu, Hai, et al.. (2022). A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction. Applied Artificial Intelligence. 36(1). 1 indexed citations

14.

Shu, Hai, et al.. (2021). A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation. Lecture notes in computer science. 2020. 435–447. 33 indexed citations

15.

Shu, Hai, Peng Wei, Kim‐Anh Do, et al.. (2021). A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis. Radiology Artificial Intelligence. 3(4). e200097–e200097. 5 indexed citations

16.

Zhong, Liming, Tengfei Li, Hai Shu, et al.. (2020). (TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients. NeuroImage. 223. 117368–117368. 12 indexed citations

17.

Shu, Hai, et al.. (2020). Comprehensive transcriptome analyses of different Crocus flower tissues uncover genes involved in crocin biosynthesis. Biologia Plantarum. 64. 504–511. 1 indexed citations

18.

Shu, Hai & Bin Nan. (2019). Estimation of large covariance and precision matrices from temporally dependent observations. The Annals of Statistics. 47(3). 12 indexed citations

19.

Li, Tengfei, Fan Zhou, Ziliang Zhu, Hai Shu, & Hongtu Zhu. (2018). A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation. PubMed. 2018. 692–695. 8 indexed citations

20.

Shu, Hai, Xiao Wang, & Hongtu Zhu. (2018). D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets. Journal of the American Statistical Association. 115(529). 292–306. 21 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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