Fenghe Tang
Impact in
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- Brain Tumor Detection and Classification
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
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- AI in cancer detection 5
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- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 2
- Co-authors
- Jianrui Ding (6 shared papers)Lingtao Wang (3 shared papers)Chunping Ning (4 shared papers)Min Xian (1 shared paper)Quan Quan (2 shared papers)S. Kevin Zhou (5 shared papers)Shili Zhao (1 shared paper)Ke Chen (1 shared paper)
- Journals
- Medical Image Analysis (2 papers)npj Digital Medicine (1 paper)Nature Communications (1 paper)Neural Processing Letters (1 paper)Journal of Physics Conference Series (1 paper)
- Partner nations
- ChinaUnited StatesBangladesh
In The Last Decade
Fenghe Tang
7 papers receiving 134 citations
Fenghe Tang's Hit Papers
Peers
Comparison fields: 5 of 37
- Neurology 28
- Computer Vision and Pattern Recognition 62
- Radiology, Nuclear Medicine and Imaging 38
- Artificial Intelligence 51
- Industrial and Manufacturing Engineering 10
Countries citing papers authored by Fenghe Tang
This map shows the geographic impact of Fenghe Tang'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 Fenghe Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fenghe Tang more than expected).
Fields of papers citing papers by Fenghe Tang
This network shows the impact of papers produced by Fenghe Tang. 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 Fenghe Tang. The network helps show where Fenghe Tang may publish in the future.
Co-authors
The 17 scholars most cited alongside Fenghe Tang, 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 | 2023 | 59 | |
| 2 | CMUNEXT: An Efficient Medical Image Segmentation Network Based on Large Kernel and Skip Fusion Hit paper breakdown → | 2024 | 58 |
| 3 | 2021 | 4 | |
| 4 | 2022 | 4 | |
| 5 | 2025 | 3 | |
| 6 | 2025 | 3 | |
| 7 | 2025 | 3 | |
| 8 | 2025 | 0 | |
| 9 | 2025 | 0 | |
| 10 | 2025 | 0 |
About Fenghe Tang
Fenghe Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Networks and Communications, having authored 10 papers that have together received 134 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Advanced Neural Network Applications (3 papers), Medical Image Segmentation Techniques (2 papers), Medical Imaging and Analysis (2 papers), COVID-19 diagnosis using AI (2 papers), Ultrasound and Hyperthermia Applications (1 paper) and Network Security and Intrusion Detection (1 paper). The work is most often cited by research in Neurology (28 citations), Computer Vision and Pattern Recognition (62 citations), Radiology, Nuclear Medicine and Imaging (38 citations), Artificial Intelligence (51 citations) and Industrial and Manufacturing Engineering (10 citations). Fenghe Tang has collaborated with scholars based in China, United States and Bangladesh. Frequent co-authors include Jianrui Ding, Lingtao Wang, Chunping Ning, Min Xian, Quan Quan, S. Kevin Zhou, Shili Zhao, Ke Chen, Quan Quan and Wei Liu. Their work appears in journals such as Medical Image Analysis, npj Digital Medicine, Nature Communications, Neural Processing Letters and Journal of Physics Conference Series.
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.