Junyu Chen
Impact in
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Neurology top 10%
- Brain Tumor Detection and Classification
Papers in
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- Medical Imaging Techniques and Applications 4
- Radiomics and Machine Learning in Medical Imaging 1
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- Medical Image Segmentation Techniques 4
- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Yongchang Du (4 shared papers)Yufan He (2 shared papers)Ye Li (3 shared papers)Eric C. Frey (3 shared papers)W. Paul Segars (1 shared paper)Ce Wang (1 shared paper)S. Kevin Zhou (1 shared paper)Yucheng Tang (1 shared paper)
- Journals
- Medical Image Analysis (3 papers)Medical Physics (1 paper)Journal of Visual Communication and Image Representation (1 paper)NeuroImage (1 paper)Journal of medical imaging (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Junyu Chen
7 papers receiving 772 citations
Junyu Chen's Hit Papers
Peers
Comparison fields: 5 of 86
- Computer Vision and Pattern Recognition 397
- Neurology 119
- Radiology, Nuclear Medicine and Imaging 276
- Health Informatics 12
- Artificial Intelligence 174
Countries citing papers authored by Junyu Chen
This map shows the geographic impact of Junyu 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 Junyu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junyu Chen more than expected).
Fields of papers citing papers by Junyu Chen
This network shows the impact of papers produced by Junyu 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 Junyu Chen. The network helps show where Junyu Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Junyu Chen, 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 | TransMorph: Transformer for unsupervised medical image registration Hit paper breakdown → | 2022 | 446 |
| 2 | 2023 | 251 | |
| 3 | 2024 | 61 | |
| 4 | 2023 | 11 | |
| 5 | 2024 | 9 | |
| 6 | 2021 | 6 | |
| 7 | 2023 | 1 | |
| 8 | 2026 | 0 | |
| 9 | 2025 | 0 |
About Junyu Chen
Junyu Chen is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Neurology, Health Informatics and Pediatrics, Perinatology and Child Health, having authored 9 papers that have together received 785 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (4 papers), Medical Imaging Techniques and Applications (4 papers), Advanced X-ray and CT Imaging (2 papers), Brain Tumor Detection and Classification (2 papers), Advanced Neural Network Applications (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Pediatric Urology and Nephrology Studies (1 paper) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (397 citations), Neurology (119 citations), Radiology, Nuclear Medicine and Imaging (276 citations), Health Informatics (12 citations) and Artificial Intelligence (174 citations). Junyu Chen has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Yongchang Du, Yufan He, Ye Li, Eric C. Frey, W. Paul Segars, Ce Wang, S. Kevin Zhou, Yucheng Tang, Bennett A. Landman and Aaron Carass. Their work appears in journals such as Medical Image Analysis, Medical Physics, Journal of Visual Communication and Image Representation, NeuroImage and Journal of medical imaging.
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.