Qi Song
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 5%
- Pulmonary and Respiratory Medicine top 10%
- Oncology
- Neurology top 10%
- Co-authors
- Milan SonkaDavid J. Nikolic‐PatersonR. C. AtkinsHui Y. LanJohn M. BuattiMona K. GarvinXiaodong WuJoseph M. Reinhardt
- Topics
- Medical Image Segmentation Techniques (13 papers)Radiomics and Machine Learning in Medical Imaging (8 papers)Lymphoma Diagnosis and Treatment (8 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingHealth InformaticsComputer Vision and Pattern Recognition
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Qi Song
57 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 107
- Radiology, Nuclear Medicine and Imaging 584
- Computer Vision and Pattern Recognition 298
- Pulmonary and Respiratory Medicine 207
- Oncology 190
- Neurology 169
Countries citing papers authored by Qi Song
This map shows the geographic impact of Qi 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 Qi Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qi Song more than expected).
Fields of papers citing papers by Qi Song
This network shows the impact of papers produced by Qi 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 Qi Song. The network helps show where Qi Song may publish in the future.
Co-authorship network of co-authors of Qi Song
This figure shows the co-authorship network connecting the top 25 collaborators of Qi Song. A scholar is included among the top collaborators of Qi 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 Qi Song. Qi 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 | 4 | |
| 3 | 44 | |
| 4 | 14 | |
| 5 | 3 | |
| 6 | 36 | |
| 7 | Learn To Be Uncertain: Leveraging Uncertain Labels In Chest X-rays With Bayesian Neural Networks | 2 |
| 8 | 170 | |
| 9 | 74 | |
| 10 | 28 | |
| 11 | 6 | |
| 12 | 41 | |
| 13 | 79 | |
| 14 | 56 | |
| 15 | 3 | |
| 16 | 12 | |
| 17 | 6 | |
| 18 | 13 | |
| 19 | 9 | |
| 20 | 26 |
About Qi Song
Qi Song is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Computer Vision and Pattern Recognition, having authored 61 papers that have together received 1.4k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (13 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and Lymphoma Diagnosis and Treatment (8 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (584 citations), Health Informatics (25 citations) and Computer Vision and Pattern Recognition (298 citations). Qi Song has collaborated with scholars based in China, United States and France. Frequent co-authors include Milan Sonka, David J. Nikolic‐Paterson, R. C. Atkins, Hui Y. Lan, John M. Buatti, Mona K. Garvin, Xiaodong Wu, Joseph M. Reinhardt, John E. Bayouth and Kemin Chen. Their work appears in journals such as Cancer Cell, Scientific Reports and Journal of the American Society of Nephrology.
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.