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 4
- Medical Image Segmentation Techniques 3
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- AI in cancer detection 4
- Cognitive Computing and Networks 1
- 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)Shuqing Li (1 shared paper)
- Journals
- Expert Systems with Applications (1 paper)Medical Image Analysis (1 paper)Journal of Documentation (1 paper)Lecture notes in computer science (2 papers)網際網路技術學刊 (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Xiaowei Ding
12 papers receiving 957 citations
Xiaowei Ding's Hit Papers
Peers
Comparison fields: 5 of 93
- Health Informatics 34
- Radiology, Nuclear Medicine and Imaging 468
- Computer Vision and Pattern Recognition 415
- Neurology 91
- Artificial Intelligence 408
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 25 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 | 663 |
| 2 | 2019 | 245 | |
| 3 | 2021 | 35 | |
| 4 | 2022 | 10 | |
| 5 | 2020 | 5 | |
| 6 | 2020 | 4 | |
| 7 | 2016 | 4 | |
| 8 | 2024 | 3 | |
| 9 | 2020 | 3 | |
| 10 | 2004 | 2 | |
| 11 | 2020 | 2 | |
| 12 | 2021 | 1 | |
| 13 | 2024 | 0 |
About Xiaowei Ding
Xiaowei Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications, having authored 13 papers that have together received 977 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Advanced Neural Network Applications (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Medical Image Segmentation Techniques (3 papers), Innovation in Digital Healthcare Systems (1 paper), Dermatological and COVID-19 studies (1 paper), Diverse Approaches in Healthcare and Education Studies (1 paper) and Cognitive Computing and Networks (1 paper). The work is most often cited by research in Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (468 citations), Computer Vision and Pattern Recognition (415 citations), Neurology (91 citations) and Artificial Intelligence (408 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, Shuqing Li and Zhan Bu. Their work appears in journals such as Expert Systems with Applications, Medical Image Analysis, Journal of Documentation, Lecture notes in computer science and 網際網路技術學刊.
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.