Fengbei Liu
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 10%
- AI in cancer detection
- Domain Adaptation and Few-Shot Learning
Papers in
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- AI in cancer detection 2
- Neural Networks and Applications 1
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- Advanced Neural Network Applications 2
- Digital Imaging for Blood Diseases 1
- Co-authors
- Gustavo Carneiro (10 shared papers)Yuyuan Liu (5 shared papers)Yuanhong Chen (4 shared papers)Vasileios Belagiannis (2 shared papers)Yu Tian (2 shared papers)Helen Frazer (6 shared papers)Chong Wang (6 shared papers)Davis J. McCarthy (4 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (2 papers)Medical Image Analysis (2 papers)IEEE Journal of Biomedical and Health Informatics (1 paper)IEEE Access (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- AustraliaUnited KingdomUnited States
In The Last Decade
Fengbei Liu
10 papers receiving 329 citations
Fengbei Liu's Hit Papers
Peers
Comparison fields: 5 of 69
- Computer Vision and Pattern Recognition 177
- Artificial Intelligence 176
- Health Informatics 7
- Radiology, Nuclear Medicine and Imaging 104
- Neurology 33
Countries citing papers authored by Fengbei Liu
This map shows the geographic impact of Fengbei Liu'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 Fengbei Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fengbei Liu more than expected).
Fields of papers citing papers by Fengbei Liu
This network shows the impact of papers produced by Fengbei Liu. 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 Fengbei Liu. The network helps show where Fengbei Liu may publish in the future.
Co-authors
The 21 scholars most cited alongside Fengbei Liu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation Hit paper breakdown → | 2022 | 180 |
| 2 | 2022 | 76 | |
| 3 | 2023 | 24 | |
| 4 | 2020 | 18 | |
| 5 | 2023 | 17 | |
| 6 | 2025 | 5 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 4 | |
| 9 | 2025 | 2 | |
| 10 | 2025 | 2 |
About Fengbei Liu
Fengbei Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Surgery, having authored 10 papers that have together received 333 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), AI in cancer detection (2 papers), Advanced Neural Network Applications (2 papers), Total Knee Arthroplasty Outcomes (1 paper), Speech and Audio Processing (1 paper), Digital Imaging for Blood Diseases (1 paper), Neural Networks and Applications (1 paper) and Brain Tumor Detection and Classification (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (177 citations), Artificial Intelligence (176 citations), Health Informatics (7 citations), Radiology, Nuclear Medicine and Imaging (104 citations) and Neurology (33 citations). Fengbei Liu has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include Gustavo Carneiro, Yuyuan Liu, Yuanhong Chen, Vasileios Belagiannis, Yu Tian, Helen Frazer, Chong Wang, Davis J. McCarthy, Yu Tian and Guansong Pang. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, IEEE Journal of Biomedical and Health Informatics, IEEE Access and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.