Shandong Wu

3.8k total citations · 1 hit paper
105 papers, 2.5k citations indexed

About

Shandong Wu is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Shandong Wu has authored 105 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Radiology, Nuclear Medicine and Imaging, 48 papers in Artificial Intelligence and 24 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Shandong Wu's work include Radiomics and Machine Learning in Medical Imaging (39 papers), AI in cancer detection (38 papers) and MRI in cancer diagnosis (17 papers). Shandong Wu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (39 papers), AI in cancer detection (38 papers) and MRI in cancer diagnosis (17 papers). Shandong Wu collaborates with scholars based in United States, China and Hong Kong. Shandong Wu's co-authors include Mubarak Shah, Brian E. Moore, Aly A. Mohamed, Dooman Arefan, Wendie A. Berg, Jules H. Sumkin, Margarita L. Zuley, Despina Kontos, Rachel C. Jankowitz and Susan P. Weinstein and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Shandong Wu

98 papers receiving 2.5k citations

Hit Papers

Chaotic invariants of Lagrangian particle trajectories fo... 2010 2026 2015 2020 2010 100 200 300
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. Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes
    2010 374 citations Shandong Wu, Mubarak Shah et al. profile →

Peers

Shandong Wu
Ashish Sharma United States
Xiao Han China
Jianpeng Zhang China
Ulaş Bağcı United States
Evangelia I. Zacharaki Greece
Volodymyr Kuleshov United States
Robert Martí Spain
Zafer Cömert Türkiye
Stavroula Mougiakakou Switzerland
Ashish Sharma United States
Shandong Wu
Citations per year, relative to Shandong Wu Shandong Wu (= 1×) peers Ashish Sharma

Countries citing papers authored by Shandong Wu

Since Specialization
Citations

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

Fields of papers citing papers by Shandong Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shandong Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Shandong Wu. A scholar is included among the top collaborators of Shandong Wu 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 Shandong Wu. Shandong Wu 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.
Kirschen, Matthew P., Jonathan Elmer, Dooman Arefan, et al.. (2025). Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest. Resuscitation. 215. 110693–110693.
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Arefan, Dooman, et al.. (2024). Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk. Radiology. 310(1). e230269–e230269. 4 indexed citations
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Sun, Min, et al.. (2024). Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study. Breast Cancer Research. 26(1). 82–82. 4 indexed citations
6.
Arefan, Dooman, Matthew Pease, Shawn R. Eagle, David O. Okonkwo, & Shandong Wu. (2023). Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury. Neurosurgical FOCUS. 54(6). E14–E14. 7 indexed citations
7.
Elmer, Jonathan, et al.. (2023). Interpretable machine learning model for imaging-based outcome prediction after cardiac arrest. Resuscitation. 191. 109894–109894. 3 indexed citations
8.
Wu, Shandong, Xinjian Chen, Xiaodong Yang, et al.. (2022). SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis. IEEE Transactions on Cybernetics. 54(4). 2345–2357. 5 indexed citations
9.
Liu, Yao, Chunjun Sheng, Wenhuan Feng, et al.. (2022). A multi-center study on glucometabolic response to bariatric surgery for different subtypes of obesity. Frontiers in Endocrinology. 13. 989202–989202. 7 indexed citations
10.
Elmer, Jonathan, Matthew Pease, Dooman Arefan, et al.. (2022). Deep learning of early brain imaging to predict post-arrest electroencephalography. Resuscitation. 172. 17–23. 6 indexed citations
11.
Liu, Guanyi, et al.. (2021). Comparison of lateral entry and crossed entry pinning for pediatric supracondylar humeral fractures: a meta-analysis of randomized controlled trials. Journal of Orthopaedic Surgery and Research. 16(1). 366–366. 10 indexed citations
12.
Bou‐Samra, Patrick, Lauren V. Huckaby, Robert Handzel, et al.. (2021). Leveraging Decision Curve Analysis to Improve Clinical Application of Surgical Risk Calculators. Journal of Surgical Research. 261. 58–66. 7 indexed citations
13.
Zheng, Jian, Shandong Wu, Ke Jiang, et al.. (2020). 3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection. IEEE Journal of Biomedical and Health Informatics. 25(3). 764–773. 9 indexed citations
14.
Hu, Yang, et al.. (2020). COX-2 in liver fibrosis. Clinica Chimica Acta. 506. 196–203. 67 indexed citations
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Mohamed, Aly A., et al.. (2018). Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clinical Cancer Research. 24(23). 5902–5909. 96 indexed citations
17.
Ma, Wenjuan, Yu Ji, Xinpeng Guo, et al.. (2018). Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features. Academic Radiology. 26(2). 196–201. 106 indexed citations
18.
Shi, Wei, et al.. (2018). Robust UAV-based tracking using hybrid classifiers. Machine Vision and Applications. 30(1). 125–137. 8 indexed citations
19.
Schmitz, Kathryn H., Nancy I. Williams, Despina Kontos, et al.. (2015). Women In Steady Exercise Research (WISER) Sister: Study design and methods. Contemporary Clinical Trials. 41. 17–30. 20 indexed citations
20.
Khan, Saad, et al.. (2011). Distinctive Left-Sided Distribution of Adrenergic-Derived Cells in the Adult Mouse Heart. PLoS ONE. 6(7). e22811–e22811. 62 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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