Bingzhi Chen
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Neurology top 5%
- Experimental and Cognitive Psychology top 10%
- Topics
- Emotion and Mood Recognition (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Medical Image Segmentation Techniques (4 papers)
- Partner nations
- ChinaSingaporeUnited Kingdom
In The Last Decade
Bingzhi Chen
20 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Computer Vision and Pattern Recognition 642
- Artificial Intelligence 480
- Radiology, Nuclear Medicine and Imaging 432
- Neurology 179
- Experimental and Cognitive Psychology 150
Countries citing papers authored by Bingzhi Chen
This map shows the geographic impact of Bingzhi Chen'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 Bingzhi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bingzhi Chen more than expected).
Fields of papers citing papers by Bingzhi Chen
This network shows the impact of papers produced by Bingzhi Chen. 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 Bingzhi Chen. The network helps show where Bingzhi Chen may publish in the future.
Co-authorship network of co-authors of Bingzhi Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Bingzhi Chen. A scholar is included among the top collaborators of Bingzhi Chen 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 Bingzhi Chen. Bingzhi Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | 0 | |
| 11 | TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentationbreakdown → | 188 |
| 12 | 44 | |
| 13 | DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentationbreakdown → | 601 |
| 14 | 42 | |
| 15 | 48 | |
| 16 | 39 | |
| 17 | 14 | |
| 18 | 99 | |
| 19 | 2 | |
| 20 | 63 |
About Bingzhi Chen
Bingzhi Chen is a scholar working on Computer Vision and Pattern Recognition, General Dentistry and Artificial Intelligence, having authored 25 papers that have together received 1.3k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Image Segmentation Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (642 citations), Neurology (179 citations) and Radiology, Nuclear Medicine and Imaging (432 citations). Bingzhi Chen has collaborated with scholars based in China, Singapore and United Kingdom. Frequent co-authors include Guangming Lu, Zheng Zhang, David Zhang, Jiayu Xu, Jinxing Li, Adams Wai‐Kin Kong, Yingjian Li, Yao Lu, Jiahui Pan and Zhihang Zhang. Their work appears in journals such as IEEE Access, Neurocomputing and IEEE Transactions on Circuits and Systems for Video Technology.
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.