Shanshan Wang
Impact in
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- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Health Informatics top 2%
Papers in
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- Advanced MRI Techniques and Applications 39
- Medical Imaging Techniques and Applications 36
- Radiomics and Machine Learning in Medical Imaging 22
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- Image and Signal Denoising Methods 23
- Medical Image Segmentation Techniques 17
Shanshan Wang
377 papers receiving 7.8k citations
Hit Papers
Peers
Comparison fields: 5 of 195
- Radiology, Nuclear Medicine and Imaging 2.2k
- Health Informatics 73
- Computer Vision and Pattern Recognition 1.0k
- Neurology 348
- Computational Mathematics 23
Countries citing papers authored by Shanshan Wang
This map shows the geographic impact of Shanshan Wang'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 Shanshan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shanshan Wang more than expected).
Fields of papers citing papers by Shanshan Wang
This network shows the impact of papers produced by Shanshan Wang. 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 Shanshan Wang. The network helps show where Shanshan Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shanshan Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 11 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 9 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 9 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 2 | |
| 13 | 2023 | 3 | |
| 14 | 2023 | 13 | |
| 15 | 2023 | 6 | |
| 16 | 2023 | 6 | |
| 17 | 2023 | 0 | |
| 18 | 2023 | 14 | |
| 19 | 2020 | 5 | |
| 20 | 2013 | 10 |
About Shanshan Wang
Shanshan Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Neurology, Computational Mathematics and Ophthalmology, having authored 416 papers that have together received 7.9k indexed citations. Recurring topics across this work include Advanced MRI Techniques and Applications (39 papers), Medical Imaging Techniques and Applications (36 papers), Sparse and Compressive Sensing Techniques (25 papers), Image and Signal Denoising Methods (23 papers), Radiomics and Machine Learning in Medical Imaging (22 papers), Medical Image Segmentation Techniques (17 papers), Photoacoustic and Ultrasonic Imaging (16 papers) and Brain Tumor Detection and Classification (13 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (2.2k citations), Health Informatics (73 citations), Computer Vision and Pattern Recognition (1.0k citations), Neurology (348 citations) and Computational Mathematics (23 citations). Shanshan Wang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Dong Liang, Qiegen Liu, Hairong Zheng, Leslie Ying, Dagan Feng, Xi Peng, Cheng Li, Shun Zhu, Feng Liang and Yong Xia. Their work appears in journals such as IEEE Transactions on Medical Imaging, Journal of Visual Communication and Image Representation, Biomedical Signal Processing and Control, PLoS ONE and NMR in Biomedicine.
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.