Qi Song

2.4k citations
8 papers · 1.6k indexed · 1 hit paper · h-index 5
Topics
Radiomics and Machine Learning in Medical Imaging (2 papers)RNA and protein synthesis mechanisms (2 papers)RNA modifications and cancer (1 paper)
Journals
SHILAP Revista de lepidopterologíaRadiologyLab on a Chip
Partner nations
ChinaUnited StatesMacao

In The Last Decade

Qi Song

8 papers receiving 1.5k citations

Hit Papers

Using Artificial Intelligence to Detect COVID-19 and Comm...202020262022202420204008001.2k

Peers

Qi Song
Comparison fields: 5 of 113
  • Radiology, Nuclear Medicine and Imaging 1.3k
  • Artificial Intelligence 593
  • Infectious Diseases 315
  • Health Informatics 300
  • Biomedical Engineering 256
Replace Junjie Bai with:
Junjie Bai China
Zhenghan Fang China
Daliang Liu China
Qizhong Xu China
Lixin Qin China
Juan Xia China
Zhong Qiu Lin China
Qi Song relative to Junjie Bai China Junjie Bai's profile →
Citations per field
00.5×1.5×
Junjie Bai · 1×
Citations per year

Countries citing papers authored by Qi Song

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

8 of 8 papers shown
#WorkIndexed citations
1 1
2 5
3 4
4 194
5 4
6
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracybreakdown →
1309
7 59
8 9

About Qi Song

Qi Song is a scholar working on Infectious Diseases, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 8 papers that have together received 1.6k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (2 papers), RNA and protein synthesis mechanisms (2 papers) and RNA modifications and cancer (1 paper). The work is most often cited by research in Health Informatics (300 citations), Radiology, Nuclear Medicine and Imaging (1.3k citations) and Artificial Intelligence (593 citations). Qi Song has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Xin Wang, Youbing Yin, Bin Kong, Yi Lu, Junjie Bai, Kunlin Cao, Guisheng Wang, Lixin Qin, Daliang Liu and Jun Xia. Their work appears in journals such as SHILAP Revista de lepidopterología, Radiology and Lab on a Chip.

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.

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