Jiangdian Song
- Radiology, Nuclear Medicine and Imaging top 1%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering top 10%
- Oncology
- Topics
- Radiomics and Machine Learning in Medical Imaging (36 papers)Lung Cancer Diagnosis and Treatment (18 papers)COVID-19 diagnosis using AI (10 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsClinical Cancer Research
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jiangdian Song
36 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 84
- Radiology, Nuclear Medicine and Imaging 1.2k
- Pulmonary and Respiratory Medicine 696
- Artificial Intelligence 285
- Biomedical Engineering 285
- Oncology 163
Countries citing papers authored by Jiangdian Song
This map shows the geographic impact of Jiangdian 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 Jiangdian Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiangdian Song more than expected).
Fields of papers citing papers by Jiangdian Song
This network shows the impact of papers produced by Jiangdian 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 Jiangdian Song. The network helps show where Jiangdian Song may publish in the future.
Co-authorship network of co-authors of Jiangdian Song
This figure shows the co-authorship network connecting the top 25 collaborators of Jiangdian Song. A scholar is included among the top collaborators of Jiangdian 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 Jiangdian Song. Jiangdian 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 | 0 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 8 | |
| 8 | 7 | |
| 9 | 4 | |
| 10 | 5 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 30 | |
| 14 | 23 | |
| 15 | 74 | |
| 16 | 2 | |
| 17 | 59 | |
| 18 | 149 | |
| 19 | 99 | |
| 20 | 123 |
About Jiangdian Song
Jiangdian Song is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Pulmonary and Respiratory Medicine, having authored 40 papers that have together received 1.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (36 papers), Lung Cancer Diagnosis and Treatment (18 papers) and COVID-19 diagnosis using AI (10 papers). The work is most often cited by research in Health Informatics (82 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations) and Pulmonary and Respiratory Medicine (696 citations). Jiangdian Song has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jie Tian, Di Dong, Mengjie Fang, Zhenyu Liu, Weimin Li, Zhihui Chang, Lei Cui, Zhaoyu Liu, Liwen Zhang and Zaiyi Liu. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Clinical Cancer Research.
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.