Eugene Vorontsov
- Health Informatics top 0.5%
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- Radiomics and Machine Learning in Medical Imaging 8
- Medical Imaging Techniques and Applications 2
- Artificial Intelligence top 5%
- AI in cancer detection 4
- Neural Networks and Applications 2
- Neurology top 10%
- Brain Tumor Detection and Classification 2
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- Advanced Neural Network Applications 5
- Generative Adversarial Networks and Image Synthesis 2
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- Medical Imaging and Analysis 4
Eugene Vorontsov
13 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Health Informatics 176
- Radiology, Nuclear Medicine and Imaging 742
- Artificial Intelligence 353
- Neurology 84
- Computer Vision and Pattern Recognition 195
Countries citing papers authored by Eugene Vorontsov
This map shows the geographic impact of Eugene Vorontsov'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 Eugene Vorontsov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Vorontsov more than expected).
Fields of papers citing papers by Eugene Vorontsov
This network shows the impact of papers produced by Eugene Vorontsov. 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 Eugene Vorontsov. The network helps show where Eugene Vorontsov may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Eugene Vorontsov, 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 | 2024 | 3 | |
| 2 | 2022 | 10 | |
| 3 | 2022 | 10 | |
| 4 | 2022 | 19 | |
| 5 | 2022 | 0 | |
| 6 | 2021 | 3 | |
| 7 | 2019 | 95 | |
| 8 | 2019 | 33 | |
| 9 | Boosting segmentation with weak supervision from image-to-image translation. | 2019 | 4 |
| 10 | Deep Learning: A Primer for Radiologistsbreakdown → | 2017 | 788 |
| 11 | On orthogonality and learning recurrent networks with long term dependencies | 2017 | 28 |
| 12 | 2017 | 181 | |
| 13 | 2016 | 18 | |
| 14 | 2015 | 20 |
About Eugene Vorontsov
Eugene Vorontsov is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Neurology, having authored 14 papers that have together received 1.2k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Advanced Neural Network Applications (5 papers), AI in cancer detection (4 papers), Medical Imaging and Analysis (4 papers), Medical Imaging Techniques and Applications (2 papers), Neural Networks and Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Brain Tumor Detection and Classification (2 papers). The work is most often cited by research in Health Informatics (176 citations), Radiology, Nuclear Medicine and Imaging (742 citations) and Artificial Intelligence (353 citations). Eugene Vorontsov has collaborated with scholars based in Canada, United States and Slovakia. Frequent co-authors include Samuel Kadoury, An Tang, Michal Drozdzal, Gabriel Chartrand, Christopher Pal, Simon Turcotte, Phillip M. Cheng, Lisa Di Jorio, Yoshua Bengio and Chris Pal. Their work appears in journals such as Medical Image Analysis, Radiology Artificial Intelligence, Scientific Reports, Journal of Applied Clinical Medical Physics and Radiographics.
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.