Qingfeng Wang
Impact in
- Polymers and Plastics top 10%
- Flame retardant materials and properties
- Synthesis and properties of polymers
-
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- COVID-19 diagnosis using AI 10
- Radiomics and Machine Learning in Medical Imaging 7
-
- Lung Cancer Diagnosis and Treatment 11
- Co-authors
- Wenfang Shi (3 shared papers)Xuehai Zhou (10 shared papers)Changlong Li (8 shared papers)Jun Huang (21 shared papers)Jie‐Zhi Cheng (10 shared papers)Chao Wang (3 shared papers)Qican Zhang (1 shared paper)Qiyu Liu (5 shared papers)
In The Last Decade
Qingfeng Wang
40 papers receiving 599 citations
Peers
Comparison fields: 5 of 100
- Polymers and Plastics 166
- Radiology, Nuclear Medicine and Imaging 123
- Computer Vision and Pattern Recognition 122
- Artificial Intelligence 108
- Pulmonary and Respiratory Medicine 86
Countries citing papers authored by Qingfeng Wang
This map shows the geographic impact of Qingfeng 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 Qingfeng Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingfeng Wang more than expected).
Fields of papers citing papers by Qingfeng Wang
This network shows the impact of papers produced by Qingfeng 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 Qingfeng Wang. The network helps show where Qingfeng Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Qingfeng 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
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 101 | |
| 2 | 2019 | 91 | |
| 3 | 2005 | 57 | |
| 4 | 2018 | 56 | |
| 5 | 2006 | 43 | |
| 6 | 2017 | 32 | |
| 7 | 2020 | 29 | |
| 8 | 2010 | 28 | |
| 9 | 2023 | 28 | |
| 10 | 2017 | 25 | |
| 11 | 2019 | 22 | |
| 12 | 2019 | 9 | |
| 13 | 2019 | 8 | |
| 14 | 2018 | 7 | |
| 15 | 2018 | 7 | |
| 16 | 2020 | 6 | |
| 17 | 2022 | 5 | |
| 18 | 2017 | 5 | |
| 19 | 2018 | 5 | |
| 20 | 2019 | 5 |
About Qingfeng Wang
Qingfeng Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence, having authored 45 papers that have together received 613 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (11 papers), COVID-19 diagnosis using AI (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Model Reduction and Neural Networks (4 papers), AI in cancer detection (4 papers), Flame retardant materials and properties (3 papers), Cloud Computing and Resource Management (3 papers) and Caching and Content Delivery (3 papers). The work is most often cited by research in Polymers and Plastics (166 citations), Radiology, Nuclear Medicine and Imaging (123 citations), Computer Vision and Pattern Recognition (122 citations), Artificial Intelligence (108 citations) and Pulmonary and Respiratory Medicine (86 citations). Qingfeng Wang has collaborated with scholars based in China, Hong Kong and Singapore. Frequent co-authors include Wenfang Shi, Xuehai Zhou, Changlong Li, Jun Huang, Jie‐Zhi Cheng, Chao Wang, Qican Zhang, Qiyu Liu, Ying Zhou and Xianyu Su. Their work appears in journals such as Physics of Fluids, IEEE Access, Polymer Degradation and Stability, BMC Medical Genomics and European Polymer Journal.
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.