Stéphane Canu
Impact in
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- Face and Expression Recognition
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Neurology top 2%
- Brain Tumor Detection and Classification
Papers in
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- Neural Networks and Applications 20
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- Face and Expression Recognition 14
- Advanced Neural Network Applications 10
- Image and Signal Denoising Methods 10
- Co-authors
- Su Ruan (11 shared papers)Tongxue Zhou (10 shared papers)Yves Grandvalet (10 shared papers)Alain Rakotomamonjy (12 shared papers)Gilles Gasso (16 shared papers)Alex Smola (3 shared papers)Cheng Soon Ong (2 shared papers)Pierre Véra (5 shared papers)
In The Last Decade
Stéphane Canu
105 papers receiving 3.0k citations
Stéphane Canu's Hit Papers
Peers
Comparison fields: 5 of 158
- Computer Vision and Pattern Recognition 1.3k
- Neurology 344
- Artificial Intelligence 1.3k
- Media Technology 178
- Signal Processing 209
Countries citing papers authored by Stéphane Canu
This map shows the geographic impact of Stéphane Canu'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 Stéphane Canu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stéphane Canu more than expected).
Fields of papers citing papers by Stéphane Canu
This network shows the impact of papers produced by Stéphane Canu. 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 Stéphane Canu. The network helps show where Stéphane Canu may publish in the future.
Co-authors
The 25 scholars most cited alongside Stéphane Canu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A review: Deep learning for medical image segmentation using multi-modality fusion Hit paper breakdown → | 2019 | 492 |
| 2 | 2007 | 224 | |
| 3 | 2009 | 221 | |
| 4 | 2021 | 161 | |
| 5 | 2004 | 154 | |
| 6 | Adaptive Scaling for Feature Selection in SVMs | 2002 | 118 |
| 7 | 2003 | 109 | |
| 8 | 1997 | 108 | |
| 9 | 2005 | 108 | |
| 10 | 2011 | 70 | |
| 11 | 2013 | 70 | |
| 12 | 2015 | 67 | |
| 13 | Support Vector Machines with a Reject Option | 2008 | 66 |
| 14 | An approach to water supply clusters by semi-supervised learning | 2010 | 53 |
| 15 | 2006 | 52 | |
| 16 | 2007 | 48 | |
| 17 | 2020 | 44 | |
| 18 | 2000 | 44 | |
| 19 | Frames, Reproducing Kernels, Regularization and Learning | 2005 | 43 |
| 20 | 2021 | 43 |
About Stéphane Canu
Stéphane Canu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Statistics and Probability and Computational Mechanics, having authored 111 papers that have together received 3.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (20 papers), Face and Expression Recognition (14 papers), Sparse and Compressive Sensing Techniques (13 papers), Advanced Neural Network Applications (10 papers), Image and Signal Denoising Methods (10 papers), Statistical Methods and Inference (10 papers), Fault Detection and Control Systems (9 papers) and Brain Tumor Detection and Classification (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Neurology (344 citations), Artificial Intelligence (1.3k citations), Media Technology (178 citations) and Signal Processing (209 citations). Stéphane Canu has collaborated with scholars based in France, Germany and Austria. Frequent co-authors include Su Ruan, Tongxue Zhou, Yves Grandvalet, Alain Rakotomamonjy, Gilles Gasso, Alex Smola, Cheng Soon Ong, Pierre Véra, Francis Bach and Xavier Mary. Their work appears in journals such as Neurocomputing, Journal of Machine Learning Research, IEEE Transactions on Image Processing, Lecture notes in computer science and Applied Stochastic Models in Business and Industry.
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.