Kun Lv

703 citations
48 papers · 438 · h-index 12

Impact in

Papers in

Kun Lv

42 papers receiving 428 citations

Peers

Kun Lv
Comparison fields: 5 of 75
  • Radiology, Nuclear Medicine and Imaging 148
  • Hepatology 48
  • Complementary and alternative medicine 37
  • Health Informatics 6
  • Biological Psychiatry 11
Replace Francesca Di Giuliano with:
Francesca Di Giuliano Italy
P. Willeke Germany
Mark Li United States
R. Carignola Italy
Jihye Song South Korea
Tokugoro Tsunematsu Japan
Yukio Sugai Japan
John Stack Ireland
Russell L. Chin United States
Yen‐Yu Chen Taiwan
Kun Lv relative to Francesca Di Giuliano Italy Francesca Di Giuliano's profile →
Citations per field
00.5×9.3×
Francesca Di Giuliano · 1×
Citations per year

Countries citing papers authored by Kun Lv

Since Specialization
Citations

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

Fields of papers citing papers by Kun Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kun Lv, 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 Kun Lv Line = papers co-authored together Kun Lv links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201059
2 202242
3 202229
4 201828
5 201922
6 202221
7 201718
8 202317
9 202316
10 202115
11 202314
12 202211
13 201711
14 202210
15 201710
16 20229
17 20209
18 20238
19 20227
20 20226

About Kun Lv

Kun Lv is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Surgery, Ophthalmology and Genetics, having authored 48 papers that have together received 438 indexed citations. Recurring topics across this work include Glaucoma and retinal disorders (7 papers), Glioma Diagnosis and Treatment (6 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), MRI in cancer diagnosis (5 papers), Retinal Diseases and Treatments (4 papers), Advanced Neuroimaging Techniques and Applications (4 papers), Corneal surgery and disorders (4 papers) and Meningioma and schwannoma management (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (148 citations), Hepatology (48 citations), Complementary and alternative medicine (37 citations), Health Informatics (6 citations) and Biological Psychiatry (11 citations). Kun Lv has collaborated with scholars based in China, United States and Bangladesh. Frequent co-authors include Daoying Geng, Maosheng Xu, Xin Cao, Yihong Fan, Jun Zhang, Huijuan Wu, Wenwen Song, Huijuan Cao, Dong Wang and Xuelian Liu. Their work appears in journals such as Medicine, Neuroradiology, NeuroImage Clinical, European Radiology and Journal of Clinical Medicine.

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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