Théo Ryffel
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
- AI in cancer detection
Papers in
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- Privacy-Preserving Technologies in Data 4
- Cryptography and Data Security 3
- Natural Language Processing Techniques 2
- Adversarial Robustness in Machine Learning 2
- Topic Modeling 2
- Machine Learning in Healthcare 1
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- Artificial Intelligence in Healthcare and Education 1
- Co-authors
- Andrew Trask (1 shared paper)Marcus R. Makowski (1 shared paper)Rickmer Braren (1 shared paper)Dmitrii Usynin (1 shared paper)Alexander Ziller (1 shared paper)Friederike Jungmann (1 shared paper)Jonathan Passerat‐Palmbach (1 shared paper)Andreas Saleh (1 shared paper)
- Journals
- Nature Machine Intelligence (1 paper)JMIR Medical Informatics (1 paper)Proceedings on Privacy Enhancing Technologies (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)
- Partner nations
- FranceUnited StatesUnited Kingdom
In The Last Decade
Théo Ryffel
6 papers receiving 334 citations
Théo Ryffel's Hit Papers
Peers
Comparison fields: 5 of 69
- Health Informatics 55
- Artificial Intelligence 244
- Radiology, Nuclear Medicine and Imaging 62
- Computer Science Applications 10
- Computer Vision and Pattern Recognition 35
Countries citing papers authored by Théo Ryffel
This map shows the geographic impact of Théo Ryffel'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 Théo Ryffel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Théo Ryffel more than expected).
Fields of papers citing papers by Théo Ryffel
This network shows the impact of papers produced by Théo Ryffel. 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 Théo Ryffel. The network helps show where Théo Ryffel may publish in the future.
Co-authors
The 20 scholars most cited alongside Théo Ryffel, 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 | End-to-end privacy preserving deep learning on multi-institutional medical imaging Hit paper breakdown → | 2021 | 270 |
| 2 | 2022 | 41 | |
| 3 | Partially Encrypted Deep Learning using Functional Encryption | 2019 | 15 |
| 4 | 2023 | 7 | |
| 5 | 2021 | 7 | |
| 6 | 2023 | 1 | |
| 7 | 2025 | 0 |
About Théo Ryffel
Théo Ryffel is a scholar working on Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Management Science and Operations Research and Infectious Diseases, having authored 7 papers that have together received 341 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Cryptography and Data Security (3 papers), Natural Language Processing Techniques (2 papers), Adversarial Robustness in Machine Learning (2 papers), Topic Modeling (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), COVID-19 diagnosis using AI (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (55 citations), Artificial Intelligence (244 citations), Radiology, Nuclear Medicine and Imaging (62 citations), Computer Science Applications (10 citations) and Computer Vision and Pattern Recognition (35 citations). Théo Ryffel has collaborated with scholars based in France, United States and United Kingdom. Frequent co-authors include Andrew Trask, Marcus R. Makowski, Rickmer Braren, Dmitrii Usynin, Alexander Ziller, Friederike Jungmann, Jonathan Passerat‐Palmbach, Andreas Saleh, Georgios Kaissis and Daniel Rueckert. Their work appears in journals such as Nature Machine Intelligence, JMIR Medical Informatics, Proceedings on Privacy Enhancing Technologies, DOAJ (DOAJ: Directory of Open Access Journals) 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.