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 12
- COVID-19 diagnosis using AI 2
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- Lung Cancer Diagnosis and Treatment 11
- Co-authors
- Robert J. Gillies (10 shared papers)Lawrence Hall (9 shared papers)Matthew B. Schabath (10 shared papers)Dmitry B. Goldgof (8 shared papers)Yoganand Balagurunathan (4 shared papers)Samuel Hawkins (3 shared papers)Qian Li (2 shared papers)Robert A. Gatenby (1 shared paper)
- Journals
- Tomography (3 papers)Scientific Reports (1 paper)Medical Physics (1 paper)Journal of Thoracic Oncology (1 paper)Computer Methods and Programs in Biomedicine (1 paper)
- Partner nations
- United StatesChinaColombia
In The Last Decade
Dmitry Cherezov
12 papers receiving 540 citations
Peers
Comparison fields: 5 of 44
- Radiology, Nuclear Medicine and Imaging 515
- Health Informatics 17
- Pulmonary and Respiratory Medicine 361
- Biomedical Engineering 170
- Artificial Intelligence 100
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 | 220 | |
| 2 | 2016 | 96 | |
| 3 | 2018 | 74 | |
| 4 | 2019 | 53 | |
| 5 | 2019 | 34 | |
| 6 | 2018 | 28 | |
| 7 | 2021 | 21 | |
| 8 | 2020 | 7 | |
| 9 | 2016 | 5 | |
| 10 | 2021 | 4 | |
| 11 | 2019 | 3 | |
| 12 | 2023 | 2 | |
| 13 | 2025 | 0 |
About Dmitry Cherezov
Dmitry Cherezov is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Surgery and Biomedical Engineering, having authored 13 papers that have together received 547 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (12 papers), Lung Cancer Diagnosis and Treatment (11 papers), AI in cancer detection (6 papers), COVID-19 diagnosis using AI (2 papers), Colorectal and Anal Carcinomas (2 papers), Advanced X-ray and CT Imaging (2 papers), Colorectal Cancer Screening and Detection (1 paper) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (515 citations), Health Informatics (17 citations), Pulmonary and Respiratory Medicine (361 citations), Biomedical Engineering (170 citations) and Artificial Intelligence (100 citations). Dmitry Cherezov has collaborated with scholars based in United States, China and Colombia. Frequent co-authors include Robert J. Gillies, Lawrence Hall, Matthew B. Schabath, Dmitry B. Goldgof, Yoganand Balagurunathan, Samuel Hawkins, Qian Li, Robert A. Gatenby, Alberto López García and Ying Liu. Their work appears in journals such as Tomography, Scientific Reports, Medical Physics, Journal of Thoracic Oncology and Computer Methods and Programs in Biomedicine.
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.