Samuel Joutard
Impact in
- Neurology top 10%
- Brain Tumor Detection and Classification
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- Medical Image Segmentation Techniques
- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
Papers in
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- Medical Image Segmentation Techniques 7
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Tom K. Vercauteren (6 shared papers)Reuben Dorent (6 shared papers)Marc Modat A (5 shared papers)Sebastien Ourselin A (5 shared papers)Satoshi Takahashi (1 shared paper)Ryuji Hamamoto (1 shared paper)Kazuma Kobayashi (1 shared paper)Masaaki Komatsu (1 shared paper)
- Journals
- Medical Image Analysis (1 paper)Biomolecules (1 paper)Lecture notes in computer science (6 papers)mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (1 paper)Research Portal (King's College London) (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Samuel Joutard
10 papers receiving 415 citations
Samuel Joutard's Hit Papers
Peers
Comparison fields: 5 of 26
- Neurology 64
- Computer Vision and Pattern Recognition 214
- Health Informatics 13
- Artificial Intelligence 25
- Radiation 5
Countries citing papers authored by Samuel Joutard
This map shows the geographic impact of Samuel Joutard'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 Samuel Joutard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Joutard more than expected).
Fields of papers citing papers by Samuel Joutard
This network shows the impact of papers produced by Samuel Joutard. 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 Samuel Joutard. The network helps show where Samuel Joutard may publish in the future.
Co-authors
The 25 scholars most cited alongside Samuel Joutard, 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 | Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning Hit paper breakdown → | 2023 | 159 |
| 2 | 2019 | 102 | |
| 3 | 2022 | 71 | |
| 4 | 2020 | 50 | |
| 5 | 2021 | 23 | |
| 6 | 2023 | 9 | |
| 7 | Permutohedral Attention Module for Efficient Non-Local Neural Networks | 2019 | 5 |
| 8 | 2022 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2024 | 1 |
About Samuel Joutard
Samuel Joutard is a scholar working on Computer Vision and Pattern Recognition, Nuclear Energy and Engineering, Waste Management and Disposal, Infectious Diseases and Organic Chemistry, having authored 10 papers that have together received 422 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Neurology (64 citations), Computer Vision and Pattern Recognition (214 citations), Health Informatics (13 citations), Artificial Intelligence (25 citations) and Radiation (5 citations). Samuel Joutard has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Tom K. Vercauteren, Reuben Dorent, Marc Modat A, Sebastien Ourselin A, Satoshi Takahashi, Ryuji Hamamoto, Kazuma Kobayashi, Masaaki Komatsu, Syuzo Kaneko and Ken Takasawa. Their work appears in journals such as Medical Image Analysis, Biomolecules, Lecture notes in computer science, mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) and Research Portal (King's College London).
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.