Benoît Schmauch
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Radiomics and Machine Learning in Medical Imaging 7
- MRI in cancer diagnosis 2
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- AI in cancer detection 5
- Co-authors
- Charlie Saillard (8 shared papers)Pierre Courtiol (6 shared papers)Julien Caldéraro (5 shared papers)Thomas Clozel (4 shared papers)Matahi Moarii (4 shared papers)Elodie Pronier (5 shared papers)Gilles Wainrib (5 shared papers)Mikhail Zaslavskiy (6 shared papers)
- Journals
- Journal of Clinical Oncology (3 papers)Diagnostic and Interventional Imaging (2 papers)Nature Communications (2 papers)Annals of Oncology (1 paper)npj Precision Oncology (1 paper)
- Partner nations
- FranceUnited States
In The Last Decade
Benoît Schmauch
13 papers receiving 828 citations
Benoît Schmauch's Hit Papers
Peers
Comparison fields: 5 of 67
- Health Informatics 55
- Radiology, Nuclear Medicine and Imaging 400
- Hepatology 108
- Biophysics 76
- Artificial Intelligence 363
Countries citing papers authored by Benoît Schmauch
This map shows the geographic impact of Benoît Schmauch'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 Benoît Schmauch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benoît Schmauch more than expected).
Fields of papers citing papers by Benoît Schmauch
This network shows the impact of papers produced by Benoît Schmauch. 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 Benoît Schmauch. The network helps show where Benoît Schmauch may publish in the future.
Co-authors
The 25 scholars most cited alongside Benoît Schmauch, 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 | A deep learning model to predict RNA-Seq expression of tumours from whole slide images Hit paper breakdown → | 2020 | 318 |
| 2 | 2020 | 221 | |
| 3 | 2019 | 119 | |
| 4 | 2019 | 94 | |
| 5 | 2016 | 37 | |
| 6 | 2020 | 15 | |
| 7 | 2023 | 13 | |
| 8 | Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment. | 2018 | 13 |
| 9 | 2023 | 6 | |
| 10 | 2021 | 3 | |
| 11 | 2020 | 2 | |
| 12 | 2023 | 1 | |
| 13 | 2022 | 1 | |
| 14 | 2025 | 0 | |
| 15 | 2021 | 0 | |
| 16 | 2025 | 0 |
About Benoît Schmauch
Benoît Schmauch is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Cancer Research, Molecular Biology and Surgery, having authored 16 papers that have together received 843 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (5 papers), Cancer Genomics and Diagnostics (2 papers), MRI in cancer diagnosis (2 papers), Gastric Cancer Management and Outcomes (2 papers), Colorectal Cancer Screening and Detection (2 papers), Molecular Biology Techniques and Applications (2 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (2 papers). The work is most often cited by research in Health Informatics (55 citations), Radiology, Nuclear Medicine and Imaging (400 citations), Hepatology (108 citations), Biophysics (76 citations) and Artificial Intelligence (363 citations). Benoît Schmauch has collaborated with scholars based in France and United States. Frequent co-authors include Charlie Saillard, Pierre Courtiol, Julien Caldéraro, Thomas Clozel, Matahi Moarii, Elodie Pronier, Gilles Wainrib, Mikhail Zaslavskiy, Alain Luciani and Simon Jégou. Their work appears in journals such as Journal of Clinical Oncology, Diagnostic and Interventional Imaging, Nature Communications, Annals of Oncology and npj Precision Oncology.
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.