Qinmu Peng
- Computer Vision and Pattern Recognition top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 5%
- Ophthalmology top 2%
- Topics
- Domain Adaptation and Few-Shot Learning (13 papers)Advanced Neuroimaging Techniques and Applications (13 papers)Functional Brain Connectivity Studies (10 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingOphthalmology
- Journals
- Proceedings of the National Academy of SciencesIEEE Transactions on Pattern Analysis and Machine IntelligenceNeuroImage
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Qinmu Peng
75 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 131
- Computer Vision and Pattern Recognition 837
- Radiology, Nuclear Medicine and Imaging 737
- Artificial Intelligence 589
- Cognitive Neuroscience 272
- Ophthalmology 260
Countries citing papers authored by Qinmu Peng
This map shows the geographic impact of Qinmu Peng'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 Qinmu Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qinmu Peng more than expected).
Fields of papers citing papers by Qinmu Peng
This network shows the impact of papers produced by Qinmu Peng. 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 Qinmu Peng. The network helps show where Qinmu Peng may publish in the future.
Co-authorship network of co-authors of Qinmu Peng
This figure shows the co-authorship network connecting the top 25 collaborators of Qinmu Peng. A scholar is included among the top collaborators of Qinmu Peng 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 Qinmu Peng. Qinmu Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 19 | |
| 13 | 10 | |
| 14 | 23 | |
| 15 | 31 | |
| 16 | 33 | |
| 17 | Subsequent Boundary Distance Regression and Pixelwise Classification Networks for Automatic Kidney Segmentation in Ultrasound Images. | 2 |
| 18 | 42 | |
| 19 | Salient region detection using local and global saliency | 2 |
| 20 | 24 |
About Qinmu Peng
Qinmu Peng is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Media Technology, having authored 78 papers that have together received 1.9k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (13 papers), Advanced Neuroimaging Techniques and Applications (13 papers) and Functional Brain Connectivity Studies (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (837 citations), Radiology, Nuclear Medicine and Imaging (737 citations) and Ophthalmology (260 citations). Qinmu Peng has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xinge You, Yiu‐ming Cheung, Jiajia Lei, Yuan Yuan, Shiming Chen, Feng Zheng, Beihao Xia, Ziming Hong, Hao Huang and Minhui Ouyang. Their work appears in journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Pattern Analysis and Machine Intelligence and NeuroImage.
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.