Ziwei Nie
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Medical Image Segmentation Techniques 5
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- Radiomics and Machine Learning in Medical Imaging 4
- Medical Imaging Techniques and Applications 3
- COVID-19 diagnosis using AI 3
- Retinal Imaging and Analysis 1
- Co-authors
- Xiaoping Yang (9 shared papers)Jun Ma (2 shared papers)Jian He (2 shared papers)Qiongjie Zhu (2 shared papers)Guoqiang Dong (2 shared papers)Jianan Chen (1 shared paper)Ziqi Yu (1 shared paper)Cheng Ge (1 shared paper)
In The Last Decade
Ziwei Nie
8 papers receiving 98 citations
Peers
Comparison fields: 5 of 22
- Health Informatics 15
- Radiology, Nuclear Medicine and Imaging 69
- Computer Vision and Pattern Recognition 38
- Artificial Intelligence 42
- Neurology 6
Countries citing papers authored by Ziwei Nie
This map shows the geographic impact of Ziwei Nie'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 Ziwei Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ziwei Nie more than expected).
Fields of papers citing papers by Ziwei Nie
This network shows the impact of papers produced by Ziwei Nie. 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 Ziwei Nie. The network helps show where Ziwei Nie may publish in the future.
Co-authors
The 22 scholars most cited alongside Ziwei Nie, 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 | Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation | 2020 | 51 |
| 2 | 2020 | 20 | |
| 3 | 2019 | 19 | |
| 4 | 2024 | 3 | |
| 5 | 2022 | 2 | |
| 6 | 2021 | 2 | |
| 7 | 2025 | 2 | |
| 8 | 2021 | 1 | |
| 9 | 2025 | 0 | |
| 10 | 2023 | 0 | |
| 11 | 2025 | 0 |
About Ziwei Nie
Ziwei Nie is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering and Computational Mechanics, having authored 11 papers that have together received 100 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Medical Imaging Techniques and Applications (3 papers), COVID-19 diagnosis using AI (3 papers), AI in cancer detection (3 papers), Advanced Numerical Analysis Techniques (1 paper), Retinal Imaging and Analysis (1 paper) and Pancreatic and Hepatic Oncology Research (1 paper). The work is most often cited by research in Health Informatics (15 citations), Radiology, Nuclear Medicine and Imaging (69 citations), Computer Vision and Pattern Recognition (38 citations), Artificial Intelligence (42 citations) and Neurology (6 citations). Ziwei Nie has collaborated with scholars based in China and Norway. Frequent co-authors include Xiaoping Yang, Jun Ma, Jian He, Qiongjie Zhu, Guoqiang Dong, Jianan Chen, Ziqi Yu, Cheng Ge, Xingle An and Yixin Wang. Their work appears in journals such as Physics in Medicine and Biology, Medical Physics, Artificial Intelligence in Medicine, IEEE Transactions on Medical Imaging and Medical Image Analysis.
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.