Xiaozheng Xie
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Ocean Engineering top 10%
- Biomedical Engineering
- Topics
- AI in cancer detection (8 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)COVID-19 diagnosis using AI (5 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsMedical Image Analysis
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Xiaozheng Xie
10 papers receiving 333 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 131
- Radiology, Nuclear Medicine and Imaging 128
- Computer Vision and Pattern Recognition 93
- Ocean Engineering 64
- Biomedical Engineering 34
Countries citing papers authored by Xiaozheng Xie
This map shows the geographic impact of Xiaozheng Xie'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 Xiaozheng Xie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaozheng Xie more than expected).
Fields of papers citing papers by Xiaozheng Xie
This network shows the impact of papers produced by Xiaozheng Xie. 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 Xiaozheng Xie. The network helps show where Xiaozheng Xie may publish in the future.
Co-authorship network of co-authors of Xiaozheng Xie
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaozheng Xie. A scholar is included among the top collaborators of Xiaozheng Xie 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 Xiaozheng Xie. Xiaozheng Xie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 13 | |
| 8 | A survey on incorporating domain knowledge into deep learning for medical image analysisbreakdown → | 210 |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 96 | |
| 12 | 11 |
About Xiaozheng Xie
Xiaozheng Xie is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 344 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and COVID-19 diagnosis using AI (5 papers). The work is most often cited by research in Health Informatics (13 citations), Radiology, Nuclear Medicine and Imaging (128 citations) and Computer Vision and Pattern Recognition (93 citations). Xiaozheng Xie has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Jianwei Niu, Shaojie Tang, Xuefeng Liu, Shui Yu, Chaochao Li, Pei Lv, Zhigang Deng, Bing Zhou, Hua Wang and Mingliang Xu. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications 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.