Charles Maussion
Impact in
- Health Informatics top 5%
- 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 6
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- Sarcoma Diagnosis and Treatment 2
- Gastric Cancer Management and Outcomes 2
- Co-authors
- Mikhail Zaslavskiy (2 shared papers)Jean‐Yves Blay (3 shared papers)Gilles Wainrib (2 shared papers)Andrew G. Nicholson (1 shared paper)Meriem Sefta (1 shared paper)Olivier Elemento (1 shared paper)Matahi Moarii (1 shared paper)Pierre Courtiol (1 shared paper)
- Journals
- Cancer Research (3 papers)Modern Pathology (2 papers)Journal of Clinical Oncology (2 papers)Nature Medicine (1 paper)Scientific Reports (1 paper)
- Partner nations
- FranceUnited KingdomUnited States
In The Last Decade
Charles Maussion
9 papers receiving 336 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Health Informatics 34
- Radiology, Nuclear Medicine and Imaging 178
- Artificial Intelligence 190
- Biophysics 32
- Cancer Research 57
Countries citing papers authored by Charles Maussion
This map shows the geographic impact of Charles Maussion'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 Charles Maussion with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Maussion more than expected).
Fields of papers citing papers by Charles Maussion
This network shows the impact of papers produced by Charles Maussion. 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 Charles Maussion. The network helps show where Charles Maussion may publish in the future.
Co-authors
The 25 scholars most cited alongside Charles Maussion, 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 | Deep learning-based classification of mesothelioma improves prediction of patient outcome Hit paper breakdown → | 2019 | 314 |
| 2 | 2023 | 10 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 1 | |
| 6 | 2025 | 1 | |
| 7 | 2024 | 1 | |
| 8 | 2023 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2025 | 0 | |
| 11 | 2024 | 0 | |
| 12 | 2022 | 0 |
About Charles Maussion
Charles Maussion is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Oncology and Cancer Research, having authored 12 papers that have together received 338 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers), Sarcoma Diagnosis and Treatment (2 papers), Gastric Cancer Management and Outcomes (2 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (1 paper), Cancer Genomics and Diagnostics (1 paper), Cell Image Analysis Techniques (1 paper) and Breast Cancer Treatment Studies (1 paper). The work is most often cited by research in Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (178 citations), Artificial Intelligence (190 citations), Biophysics (32 citations) and Cancer Research (57 citations). Charles Maussion has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Mikhail Zaslavskiy, Jean‐Yves Blay, Gilles Wainrib, Andrew G. Nicholson, Meriem Sefta, Olivier Elemento, Matahi Moarii, Pierre Courtiol, Thomas Clozel and Nolwenn Le Stang. Their work appears in journals such as Cancer Research, Modern Pathology, Journal of Clinical Oncology, Nature Medicine and Scientific Reports.
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.