Yang Song
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 10%
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Topics
- Radiomics and Machine Learning in Medical Imaging (44 papers)MRI in cancer diagnosis (24 papers)Glioma Diagnosis and Treatment (11 papers)
- Journals
- Angewandte Chemie International EditionSHILAP Revista de lepidopterologíaPLoS ONE
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Yang Song
92 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Radiology, Nuclear Medicine and Imaging 820
- Pulmonary and Respiratory Medicine 373
- Artificial Intelligence 193
- Biomedical Engineering 190
- Computer Vision and Pattern Recognition 140
Countries citing papers authored by Yang Song
This map shows the geographic impact of Yang Song'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 Yang Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Song more than expected).
Fields of papers citing papers by Yang Song
This network shows the impact of papers produced by Yang Song. 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 Yang Song. The network helps show where Yang Song may publish in the future.
Co-authorship network of co-authors of Yang Song
This figure shows the co-authorship network connecting the top 25 collaborators of Yang Song. A scholar is included among the top collaborators of Yang Song 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 Yang Song. Yang Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 19 | |
| 11 | 12 | |
| 12 | 5 | |
| 13 | 19 | |
| 14 | 5 | |
| 15 | 14 | |
| 16 | 21 | |
| 17 | Score-Based Generative Modeling through Stochastic Differential Equations | 6 |
| 18 | 188 | |
| 19 | Sliced Score Matching: A Scalable Approach to Density and Score Estimation | 5 |
| 20 | Generative Modeling by Estimating Gradients of the Data Distribution | 21 |
About Yang Song
Yang Song is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Genetics, having authored 106 papers that have together received 1.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (44 papers), MRI in cancer diagnosis (24 papers) and Glioma Diagnosis and Treatment (11 papers). The work is most often cited by research in Health Informatics (59 citations), Radiology, Nuclear Medicine and Imaging (820 citations) and Hepatology (139 citations). Yang Song has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Guang Yang, Yu‐Dong Zhang, Xu Yan, Minxiong Zhou, Ying Hou, Ye‐Feng Yao, Weidong Wang, Ziqi Lv, Yida Wang and Jing Zhang. Their work appears in journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and PLoS ONE.
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.