Junlin Yang
Impact in
-
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
Papers in
-
- Domain Adaptation and Few-Shot Learning 4
- AI in cancer detection 2
-
- Radiomics and Machine Learning in Medical Imaging 5
- COVID-19 diagnosis using AI 4
- Co-authors
- Antonio Torralba (1 shared paper)Karsten Kreis (1 shared paper)Daiqing Li (1 shared paper)Sanja Fidler (1 shared paper)Julius Chapiro (6 shared papers)MingDe Lin (5 shared papers)James S. Duncan (6 shared papers)Nicha C. Dvornek (5 shared papers)
- Journals
- Journal of Vascular and Interventional Radiology (2 papers)IEEE Transactions on Medical Imaging (1 paper)Applied Sciences (1 paper)Acta Physico-Chimica Sinica (1 paper)Computerized Medical Imaging and Graphics (1 paper)
- Partner nations
- United StatesChinaSwitzerland
In The Last Decade
Junlin Yang
12 papers receiving 256 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 123
- Health Informatics 6
- Radiology, Nuclear Medicine and Imaging 98
- Artificial Intelligence 135
- Hepatology 25
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 | 115 | |
| 2 | 2019 | 67 | |
| 3 | 2018 | 20 | |
| 4 | 2022 | 12 | |
| 5 | 2019 | 12 | |
| 6 | 2019 | 8 | |
| 7 | 2021 | 7 | |
| 8 | 2020 | 7 | |
| 9 | ShelfNet for Real-time Semantic Segmentation | 2018 | 5 |
| 10 | 2023 | 4 | |
| 11 | 2021 | 4 | |
| 12 | 2019 | 1 | |
| 13 | 2024 | 0 |
About Junlin Yang
Junlin Yang is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Hepatology and Biomedical Engineering, having authored 13 papers that have together received 262 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Hepatocellular Carcinoma Treatment and Prognosis (2 papers), AI in cancer detection (2 papers), Advanced Neural Network Applications (2 papers), Advanced Chemical Sensor Technologies (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (123 citations), Health Informatics (6 citations), Radiology, Nuclear Medicine and Imaging (98 citations), Artificial Intelligence (135 citations) and Hepatology (25 citations). Junlin Yang has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Antonio Torralba, Karsten Kreis, Daiqing Li, Sanja Fidler, Julius Chapiro, MingDe Lin, James S. Duncan, Nicha C. Dvornek, Fan Zhang and Juntang Zhuang. Their work appears in journals such as Journal of Vascular and Interventional Radiology, IEEE Transactions on Medical Imaging, Applied Sciences, Acta Physico-Chimica Sinica and Computerized Medical Imaging and Graphics.
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.