Junqiang Lei

2.1k citations
52 papers · 911 · 1 hit paper · h-index 13

Impact in

Papers in

    • Radiomics and Machine Learning in Medical Imaging 11
    • COVID-19 diagnosis using AI 7
    • MRI in cancer diagnosis 6
    • Medical Imaging Techniques and Applications 5
    • Advanced Neuroimaging Techniques and Applications 4

Junqiang Lei

46 papers receiving 889 citations

Junqiang Lei's Hit Papers

CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia 2020 · 478 citations
4780+2+4Years since publication100200300400

Peers

Junqiang Lei
Comparison fields: 5 of 100
  • Radiology, Nuclear Medicine and Imaging 467
  • Infectious Diseases 373
  • Health Informatics 25
  • Critical Care and Intensive Care Medicine 54
  • Neurology 134
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Junqiang Lei relative to Yuting Liao China Yuting Liao's profile →
Citations per field
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Yuting Liao · 1×
Citations per year

Countries citing papers authored by Junqiang Lei

Since Specialization
Citations

This map shows the geographic impact of Junqiang Lei'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 Junqiang Lei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junqiang Lei more than expected).

Fields of papers citing papers by Junqiang Lei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junqiang Lei. 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 Junqiang Lei. The network helps show where Junqiang Lei may publish in the future.

Co-authors

The 25 scholars most cited alongside Junqiang Lei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Junqiang Lei Line = papers co-authored together Junqiang Lei links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1
CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia
Hit paper breakdown →
2020478
2 201649
3 202049
4 202044
5 202127
6 202024
7 202023
8 202320
9 202018
10 201716
11 202014
12 202312
13 202012
14 202311
15 202011
16 202110
17 20249
18 20227
19 20216
20 20245

About Junqiang Lei

Junqiang Lei is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology, Pulmonary and Respiratory Medicine, Oncology and Molecular Biology, having authored 52 papers that have together received 911 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (11 papers), COVID-19 diagnosis using AI (7 papers), Hepatocellular Carcinoma Treatment and Prognosis (6 papers), MRI in cancer diagnosis (6 papers), Medical Imaging Techniques and Applications (5 papers), COVID-19 Clinical Research Studies (5 papers), Advanced Neuroimaging Techniques and Applications (4 papers) and COVID-19 and healthcare impacts (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (467 citations), Infectious Diseases (373 citations), Health Informatics (25 citations), Critical Care and Intensive Care Medicine (54 citations) and Neurology (134 citations). Junqiang Lei has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Xiaolong Qi, Junfeng Li, Xun� Li, Jinkui Li, Yaolong Chen, Yanli Zhang, Li Yang, Xiajie Wen, Hong Zhang and Yan Guo. Their work appears in journals such as Organic & Biomolecular Chemistry, European Journal of Radiology, Annals of Translational Medicine, Radiology and Scientific Reports.

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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