Mathilde Bateson
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Multimodal Machine Learning Applications
Papers in
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- Advanced Neural Network Applications 2
- Medical Image Segmentation Techniques 1
- Advanced Vision and Imaging 1
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- Radiopharmaceutical Chemistry and Applications 1
- Co-authors
- Ismail Ben Ayed (3 shared papers)Hervé Lombaert (3 shared papers)José Dolz (2 shared papers)Hoel Kervadec (2 shared papers)Éric Deutsch (1 shared paper)Christophe Massard (1 shared paper)Samy Ammari (1 shared paper)Charles Ferté (1 shared paper)
In The Last Decade
Mathilde Bateson
5 papers receiving 189 citations
Peers
Comparison fields: 5 of 49
- Radiology, Nuclear Medicine and Imaging 95
- Computer Vision and Pattern Recognition 62
- Artificial Intelligence 60
- Health Informatics 2
- Health Information Management 5
Countries citing papers authored by Mathilde Bateson
This map shows the geographic impact of Mathilde Bateson'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 Mathilde Bateson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathilde Bateson more than expected).
Fields of papers citing papers by Mathilde Bateson
This network shows the impact of papers produced by Mathilde Bateson. 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 Mathilde Bateson. The network helps show where Mathilde Bateson may publish in the future.
Co-authors
The 16 scholars most cited alongside Mathilde Bateson, 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 | 2017 | 70 | |
| 2 | 2022 | 63 | |
| 3 | 2021 | 20 | |
| 4 | 2016 | 19 | |
| 5 | 2022 | 19 | |
| 6 | KEEP ON TRACKING | 2002 | 0 |
About Mathilde Bateson
Mathilde Bateson is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology and Epidemiology, having authored 6 papers that have together received 191 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), Medical Image Segmentation Techniques (1 paper), Radiopharmaceutical Chemistry and Applications (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (95 citations), Computer Vision and Pattern Recognition (62 citations), Artificial Intelligence (60 citations), Health Informatics (2 citations) and Health Information Management (5 citations). Mathilde Bateson has collaborated with scholars based in Canada, France and Belgium. Frequent co-authors include Ismail Ben Ayed, Hervé Lombaert, José Dolz, Hoel Kervadec, Éric Deutsch, Christophe Massard, Samy Ammari, Charles Ferté, Jean‐Charles Soria and Laurent Dercle. Their work appears in journals such as Scientific Reports, PLoS ONE, IEEE Transactions on Medical Imaging, Medical Image Analysis and Lecture notes in computer science.
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.