Junyuan Shang
- Cardiology and Cardiovascular Medicine top 5%
- Cognitive Neuroscience top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Pulmonary and Respiratory Medicine
- Topics
- Domain Adaptation and Few-Shot Learning (15 papers)Multimodal Machine Learning Applications (8 papers)Machine Learning and ELM (7 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Junyuan Shang
29 papers receiving 766 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Cardiology and Cardiovascular Medicine 431
- Cognitive Neuroscience 276
- Artificial Intelligence 267
- Biomedical Engineering 110
- Pulmonary and Respiratory Medicine 106
Countries citing papers authored by Junyuan Shang
This map shows the geographic impact of Junyuan Shang'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 Junyuan Shang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junyuan Shang more than expected).
Fields of papers citing papers by Junyuan Shang
This network shows the impact of papers produced by Junyuan Shang. 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 Junyuan Shang. The network helps show where Junyuan Shang may publish in the future.
Co-authorship network of co-authors of Junyuan Shang
This figure shows the co-authorship network connecting the top 25 collaborators of Junyuan Shang. A scholar is included among the top collaborators of Junyuan Shang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Junyuan Shang. Junyuan Shang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 18 | |
| 7 | 4 | |
| 8 | 2 | |
| 9 | 24 | |
| 10 | 6 | |
| 11 | Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic reviewbreakdown → | 293 |
| 12 | 2 | |
| 13 | 61 | |
| 14 | 13 | |
| 15 | 15 | |
| 16 | 2 | |
| 17 | Knowledge Guided Multi-instance Multi-label Learning via Neural Networks in Medicines Prediction | 3 |
| 18 | 1 | |
| 19 | CGNF: Conditional Graph Neural Fields | 4 |
| 20 | 107 |
About Junyuan Shang
Junyuan Shang is a scholar working on Artificial Intelligence, Medical Laboratory Technology and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 779 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (15 papers), Multimodal Machine Learning Applications (8 papers) and Machine Learning and ELM (7 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (431 citations), Cognitive Neuroscience (276 citations) and Health Information Management (61 citations). Junyuan Shang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Shenda Hong, Cao Xiao, Jimeng Sun, Yuxi Zhou, Hongyan Li, Tengfei Ma, Junqing Xie, Qingyun Wang, Zhiheng Zhou and Moxian Song. Their work appears in journals such as Expert Systems with Applications, Sensors and Neurocomputing.
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.