Dmitry Cherezov
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- COVID-19 diagnosis using AI
- Health Informatics top 10%
Papers in
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- Radiomics and Machine Learning in Medical Imaging 11
- COVID-19 diagnosis using AI 2
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- Lung Cancer Diagnosis and Treatment 8
- Lung Cancer Treatments and Mutations 1
- Co-authors
- Robert J. Gillies (10 shared papers)Matthew B. Schabath (10 shared papers)Lawrence Hall (9 shared papers)Dmitry B. Goldgof (8 shared papers)Yoganand Balagurunathan (4 shared papers)Samuel Hawkins (3 shared papers)Qian Li (2 shared papers)Ying Liu (1 shared paper)
- Journals
- Tomography (3 papers)Medical Physics (1 paper)Computer Methods and Programs in Biomedicine (1 paper)Cancer Medicine (1 paper)IEEE Access (1 paper)
- Partner nations
- United StatesChinaUganda
In The Last Decade
Dmitry Cherezov
14 papers receiving 563 citations
Peers
Comparison fields: 5 of 48
- Radiology, Nuclear Medicine and Imaging 418
- Health Informatics 16
- Pulmonary and Respiratory Medicine 229
- Artificial Intelligence 94
- Biomedical Engineering 95
Countries citing papers authored by Dmitry Cherezov
This map shows the geographic impact of Dmitry Cherezov'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 Dmitry Cherezov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitry Cherezov more than expected).
Fields of papers citing papers by Dmitry Cherezov
This network shows the impact of papers produced by Dmitry Cherezov. 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 Dmitry Cherezov. The network helps show where Dmitry Cherezov may publish in the future.
Co-authors
The 25 scholars most cited alongside Dmitry Cherezov, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 222 | |
| 2 | 2016 | 97 | |
| 3 | 2018 | 74 | |
| 4 | 2019 | 57 | |
| 5 | 2019 | 36 | |
| 6 | 2018 | 28 | |
| 7 | 2021 | 23 | |
| 8 | 2019 | 9 | |
| 9 | 2020 | 7 | |
| 10 | 2016 | 5 | |
| 11 | 2023 | 4 | |
| 12 | 2021 | 4 | |
| 13 | 2019 | 3 | |
| 14 | 2025 | 1 | |
| 15 | 2025 | 0 |
About Dmitry Cherezov
Dmitry Cherezov is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Biomedical Engineering and Political Science and International Relations, having authored 15 papers that have together received 570 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (11 papers), Lung Cancer Diagnosis and Treatment (8 papers), AI in cancer detection (4 papers), Advanced X-ray and CT Imaging (2 papers), COVID-19 diagnosis using AI (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), Sociopolitical Dynamics in Russia (1 paper) and Lung Cancer Treatments and Mutations (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (418 citations), Health Informatics (16 citations), Pulmonary and Respiratory Medicine (229 citations), Artificial Intelligence (94 citations) and Biomedical Engineering (95 citations). Dmitry Cherezov has collaborated with scholars based in United States, China and Uganda. Frequent co-authors include Robert J. Gillies, Matthew B. Schabath, Lawrence Hall, Dmitry B. Goldgof, Yoganand Balagurunathan, Samuel Hawkins, Qian Li, Ying Liu, Robert A. Gatenby and Olya Stringfield. Their work appears in journals such as Tomography, Medical Physics, Computer Methods and Programs in Biomedicine, Cancer Medicine and IEEE Access.
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.