Chaolu Feng
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Artificial Intelligence
- Media Technology top 5%
- Neurology
- Co-authors
- Dazhe ZhaoMin HuangJinzhu YangChunming LiChristos DavatzikosXiaozhu LinXiaohua QianShaoxiang Zhang
- Topics
- Medical Image Segmentation Techniques (20 papers)Advanced Neural Network Applications (8 papers)Radiomics and Machine Learning in Medical Imaging (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingMedia Technology
- Partner nations
- ChinaUnited StatesSaint Kitts and Nevis
In The Last Decade
Chaolu Feng
50 papers receiving 494 citations
Peers
Comparison fields: 5 of 74
- Computer Vision and Pattern Recognition 297
- Radiology, Nuclear Medicine and Imaging 217
- Artificial Intelligence 91
- Media Technology 69
- Neurology 63
Countries citing papers authored by Chaolu Feng
This map shows the geographic impact of Chaolu Feng'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 Chaolu Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaolu Feng more than expected).
Fields of papers citing papers by Chaolu Feng
This network shows the impact of papers produced by Chaolu Feng. 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 Chaolu Feng. The network helps show where Chaolu Feng may publish in the future.
Co-authorship network of co-authors of Chaolu Feng
This figure shows the co-authorship network connecting the top 25 collaborators of Chaolu Feng. A scholar is included among the top collaborators of Chaolu Feng 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 Chaolu Feng. Chaolu Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 30 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 0 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | 20 | |
| 16 | 14 | |
| 17 | 3 | |
| 18 | 28 | |
| 19 | 30 | |
| 20 | 15 |
About Chaolu Feng
Chaolu Feng is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Computer Graphics and Computer-Aided Design, having authored 53 papers that have together received 505 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (20 papers), Advanced Neural Network Applications (8 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (297 citations), Radiology, Nuclear Medicine and Imaging (217 citations) and Media Technology (69 citations). Chaolu Feng has collaborated with scholars based in China, United States and Saint Kitts and Nevis. Frequent co-authors include Dazhe Zhao, Min Huang, Jinzhu Yang, Chunming Li, Christos Davatzikos, Xiaozhu Lin, Xiaohua Qian, Shaoxiang Zhang, Kun Yu and Wei Li. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Physics and Neurocomputing.
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.