Xiaowei Ding
Impact in
- Health Informatics top 5%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 2
- Image and Video Quality Assessment 1
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- AI in cancer detection 2
- Co-authors
- Nima Tajbakhsh (3 shared papers)Zhihao Wu (2 shared papers)Jeffrey N. Chiang (2 shared papers)Qian Li (1 shared paper)Kang Dang (1 shared paper)Demetri Terzopoulos (1 shared paper)Lei Shi (1 shared paper)Zhan Bu (1 shared paper)
- Journals
- Medical Image Analysis (1 paper)Expert Systems with Applications (1 paper)Journal of Documentation (1 paper)網際網路技術學刊 (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Xiaowei Ding
7 papers receiving 810 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Health Informatics 37
- Radiology, Nuclear Medicine and Imaging 441
- Computer Vision and Pattern Recognition 364
- Artificial Intelligence 364
- Neurology 87
Countries citing papers authored by Xiaowei Ding
This map shows the geographic impact of Xiaowei Ding'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 Xiaowei Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaowei Ding more than expected).
Fields of papers citing papers by Xiaowei Ding
This network shows the impact of papers produced by Xiaowei Ding. 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 Xiaowei Ding. The network helps show where Xiaowei Ding may publish in the future.
Co-authors
The 20 scholars most cited alongside Xiaowei Ding, 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 | Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation Hit paper breakdown → | 2020 | 568 |
| 2 | 2019 | 214 | |
| 3 | 2021 | 24 | |
| 4 | 2022 | 9 | |
| 5 | 2016 | 4 | |
| 6 | 2020 | 4 | |
| 7 | 2004 | 2 | |
| 8 | 2024 | 0 |
About Xiaowei Ding
Xiaowei Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Information Systems and Computer Networks and Communications, having authored 8 papers that have together received 825 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Advanced Neural Network Applications (3 papers), AI in cancer detection (2 papers), Medical Image Segmentation Techniques (2 papers), Image and Video Quality Assessment (1 paper), Expert finding and Q&A systems (1 paper), Error Correcting Code Techniques (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (37 citations), Radiology, Nuclear Medicine and Imaging (441 citations), Computer Vision and Pattern Recognition (364 citations), Artificial Intelligence (364 citations) and Neurology (87 citations). Xiaowei Ding has collaborated with scholars based in China and United States. Frequent co-authors include Nima Tajbakhsh, Zhihao Wu, Jeffrey N. Chiang, Qian Li, Qian Li, Kang Dang, Demetri Terzopoulos, Lei Shi, Zhan Bu and Shuqing Li. Their work appears in journals such as Medical Image Analysis, Expert Systems with Applications, Journal of Documentation, 網際網路技術學刊 and arXiv (Cornell University).
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.