Jun Wang: h-index, Total Citations, and Citation Map
Jun Wang's h-index is 34 (82 i10-index, 6,876+ total citations across 3+ publications) according to Google Scholar as of May 2026. Jun Wang is affiliated with Shanghai University.
Jun Wang is a researcher affiliated with Shanghai University, specializing in Machine Learning, Medical Image Classification, Medical Image Analysis. Their work has been cited 6,876 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jun Wang's Citation Metrics
Bibliometric impact based on 3 indexed publications. Of these, 2 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 34
- i10-Index
- 82
- Total Citations
- 6,876
- Citing Countries
- 17
As of May 2026.
Jun Wang has an h-index of 34 and 6,876 total citations across 3 publications, with research cited by institutions in 17 countries.
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We've mapped 5,000 of 6,876 citations for Jun Wang
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Global Impact Map
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Top Cited Works
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Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
20211,755
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
4 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed deep learning methods for quantifying abnormal lung regions in COVID-19 CT scans to predict disease severity, establishing a foundational framework for AI-driven clinical assessment.
The researcher developed a collaborative fuzzy clustering framework for multiple weighted views, establishing a foundational method for integrating heterogeneous data sources in unsupervised learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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