Junlin Yang
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Generative Adversarial Networks and Image Synthesis
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- COVID-19 diagnosis using AI 6
- Radiomics and Machine Learning in Medical Imaging 4
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- Domain Adaptation and Few-Shot Learning 6
- AI in cancer detection 3
- Co-authors
- Nicha C. Dvornek (7 shared papers)Julius Chapiro (9 shared papers)James S. Duncan (8 shared papers)Juntang Zhuang (3 shared papers)Karsten Kreis (1 shared paper)Antonio B. Torralba (1 shared paper)Sanja Fidler (1 shared paper)Daiqing Li (1 shared paper)
- Journals
- Journal of Vascular and Interventional Radiology (2 papers)Computerized Medical Imaging and Graphics (1 paper)Acta Physico-Chimica Sinica (1 paper)JHEP Reports (1 paper)Applied Sciences (1 paper)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Junlin Yang
17 papers receiving 534 citations
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 280
- Radiology, Nuclear Medicine and Imaging 150
- Health Informatics 9
- Artificial Intelligence 213
- Neurology 38
Countries citing papers authored by Junlin Yang
This map shows the geographic impact of Junlin Yang'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 Junlin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junlin Yang more than expected).
Fields of papers citing papers by Junlin Yang
This network shows the impact of papers produced by Junlin Yang. 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 Junlin Yang. The network helps show where Junlin Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Junlin Yang, 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 | 2021 | 135 | |
| 2 | 2019 | 105 | |
| 3 | 2019 | 102 | |
| 4 | 2020 | 65 | |
| 5 | 2023 | 34 | |
| 6 | 2018 | 21 | |
| 7 | 2019 | 14 | |
| 8 | 2019 | 14 | |
| 9 | 2022 | 13 | |
| 10 | 2019 | 11 | |
| 11 | 2020 | 8 | |
| 12 | 2021 | 7 | |
| 13 | 2020 | 7 | |
| 14 | 2023 | 4 | |
| 15 | 2021 | 4 | |
| 16 | 2019 | 4 | |
| 17 | 2024 | 2 | |
| 18 | 2026 | 0 |
About Junlin Yang
Junlin Yang is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Computer Vision and Pattern Recognition, Health Informatics and Catalysis, having authored 18 papers that have together received 550 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (6 papers), Domain Adaptation and Few-Shot Learning (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Advanced Neural Network Applications (3 papers), AI in cancer detection (3 papers), Medical Image Segmentation Techniques (2 papers), Catalytic Processes in Materials Science (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (280 citations), Radiology, Nuclear Medicine and Imaging (150 citations), Health Informatics (9 citations), Artificial Intelligence (213 citations) and Neurology (38 citations). Junlin Yang has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Nicha C. Dvornek, Julius Chapiro, James S. Duncan, Juntang Zhuang, Karsten Kreis, Antonio B. Torralba, Sanja Fidler, Daiqing Li, Lin Gu and MingDe Lin. Their work appears in journals such as Journal of Vascular and Interventional Radiology, Computerized Medical Imaging and Graphics, Acta Physico-Chimica Sinica, JHEP Reports and Applied Sciences.
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.