Lev Bangiyev

419 citations
27 papers · 284 · h-index 10

Impact in

Papers in

Lev Bangiyev

26 papers receiving 281 citations

Peers

Lev Bangiyev
Comparison fields: 5 of 55
  • Radiology, Nuclear Medicine and Imaging 159
  • Health Informatics 9
  • Biological Psychiatry 11
  • Genetics 36
  • Neurology 24
Replace Dingxi Liu with:
Dingxi Liu China
Shimpei Kato Japan
Chae Jung Park South Korea
Yangyang Wang China
Takuya Ishida Japan
Laurent J. Livermore United Kingdom
Xiaorui Su China
Anish Kapadia Canada
Paolo Vezzulli Italy
Sarah Schlaeger Germany
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Citations per field
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Citations per year

Countries citing papers authored by Lev Bangiyev

Since Specialization
Citations

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

Fields of papers citing papers by Lev Bangiyev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201946
2 201533
3 201929
4 201526
5 201321
6 202119
7 202017
8 202014
9 201911
10 20209
11 20218
12 20158
13 20198
14 20146
15 20205
16 20244
17 20184
18 20243
19 20203
20 20203

About Lev Bangiyev

Lev Bangiyev is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology, Pathology and Forensic Medicine, Molecular Biology and Surgery, having authored 27 papers that have together received 284 indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (6 papers), MRI in cancer diagnosis (6 papers), Advanced MRI Techniques and Applications (4 papers), Multiple Sclerosis Research Studies (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Glioma Diagnosis and Treatment (3 papers), Dementia and Cognitive Impairment Research (2 papers) and Noise Effects and Management (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (159 citations), Health Informatics (9 citations), Biological Psychiatry (11 citations), Genetics (36 citations) and Neurology (24 citations). Lev Bangiyev has collaborated with scholars based in United States, Italy and Canada. Frequent co-authors include Timothy Q. Duong, Girish Fatterpekar, Kenneth Wengler, Xiang He, Kai Tobias Block, Mark E. Schweitzer, Fernando E. Boada, Patricia K. Coyle, Turhan Canli and Sean Clouston. Their work appears in journals such as Multiple Sclerosis and Related Disorders, American Journal of Roentgenology, American Journal of Neuroradiology, Journal of Alzheimer s Disease and Clinical Breast Cancer.

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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