Raphaël Prevost
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- Medical Image Segmentation Techniques 7
- Advanced Neural Network Applications 3
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- Radiomics and Machine Learning in Medical Imaging 3
- MRI in cancer diagnosis 3
- Medical Imaging Techniques and Applications 2
- Ultrasound Imaging and Elastography 2
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- AI in cancer detection 2
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- Surgical Simulation and Training 2
- Co-authors
- Roberto ArdonBenoît MoryRémi CuingnetLaurent D. CohenDavid LesageWolfgang WeinAlexander LadikosRobert S. Bauer
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingHealth Informatics
- Journals
- Medical Image Analysis (1 paper)Lecture notes in computer science (6 papers)Eurographics (1 paper)
In The Last Decade
Raphaël Prevost
14 papers receiving 225 citations
Peers
Comparison fields: 5 of 50
- Computer Vision and Pattern Recognition 123
- Radiology, Nuclear Medicine and Imaging 119
- Health Informatics 3
- Biomedical Engineering 97
- Artificial Intelligence 39
Countries citing papers authored by Raphaël Prevost
This map shows the geographic impact of Raphaël Prevost'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 Raphaël Prevost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raphaël Prevost more than expected).
Fields of papers citing papers by Raphaël Prevost
This network shows the impact of papers produced by Raphaël Prevost. 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 Raphaël Prevost. The network helps show where Raphaël Prevost may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Raphaël Prevost, 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 | 2022 | 2 | |
| 2 | 2018 | 81 | |
| 3 | Deep Learning-Based 3D Freehand Ultrasound Reconstruction with Inertial Measurement Units | 2018 | 2 |
| 4 | 2014 | 5 | |
| 5 | 2014 | 2 | |
| 6 | 2013 | 1 | |
| 7 | 2013 | 3 | |
| 8 | 2013 | 5 | |
| 9 | 2012 | 105 | |
| 10 | 2012 | 16 | |
| 11 | KIDNEY DETECTION AND REAL-TIME SEGMENTATION IN 3D CONTRAST-ENHANCED ULTRASOUND IMAGES | 2012 | 1 |
| 12 | 2012 | 2 | |
| 13 | Template Deformation with User Constraints for Live 3D Interactive Surface Extraction | 2011 | 2 |
| 14 | 2010 | 1 |
About Raphaël Prevost
Raphaël Prevost is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computer Graphics and Computer-Aided Design, Oral Surgery and Biomedical Engineering, having authored 14 papers that have together received 228 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), MRI in cancer diagnosis (3 papers), Advanced Neural Network Applications (3 papers), Medical Imaging Techniques and Applications (2 papers), Surgical Simulation and Training (2 papers), Ultrasound Imaging and Elastography (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (123 citations), Radiology, Nuclear Medicine and Imaging (119 citations), Health Informatics (3 citations), Biomedical Engineering (97 citations) and Artificial Intelligence (39 citations). Raphaël Prevost has collaborated with scholars based in France, Germany and Austria. Frequent co-authors include Roberto Ardon, Benoît Mory, Rémi Cuingnet, Laurent D. Cohen, David Lesage, Wolfgang Wein, Alexander Ladikos, Robert S. Bauer, Oliver Zettinig and Mehrdad Salehi. Their work appears in journals such as Medical Image Analysis, Lecture notes in computer science, Eurographics, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and HAL (Le Centre pour la Communication Scientifique Directe).
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.