Thomas Clozel
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Molecular Biology
- Oncology top 10%
- Cancer Research top 10%
- Co-authors
- Gilles WainribPierre CourtiolMatahi MoariiMikhail ZaslavskiyElodie PronierOlivier ElementoMeriem SeftaCharlie Saillard
- Topics
- Radiomics and Machine Learning in Medical Imaging (4 papers)Bladder and Urothelial Cancer Treatments (4 papers)AI in cancer detection (4 papers)
- Partner nations
- United StatesFranceCanada
In The Last Decade
Thomas Clozel
17 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Radiology, Nuclear Medicine and Imaging 463
- Artificial Intelligence 439
- Molecular Biology 332
- Oncology 265
- Cancer Research 248
Countries citing papers authored by Thomas Clozel
This map shows the geographic impact of Thomas Clozel'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 Thomas Clozel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Clozel more than expected).
Fields of papers citing papers by Thomas Clozel
This network shows the impact of papers produced by Thomas Clozel. 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 Thomas Clozel. The network helps show where Thomas Clozel may publish in the future.
Co-authorship network of co-authors of Thomas Clozel
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Clozel. A scholar is included among the top collaborators of Thomas Clozel based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Clozel. Thomas Clozel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 57 | |
| 3 | 200 | |
| 4 | 48 | |
| 5 | 14 | |
| 6 | A deep learning model to predict RNA-Seq expression of tumours from whole slide imagesbreakdown → | 282 |
| 7 | 2 | |
| 8 | Deep learning-based classification of mesothelioma improves prediction of patient outcomebreakdown → | 314 |
| 9 | 11 | |
| 10 | 44 | |
| 11 | 29 | |
| 12 | 64 | |
| 13 | 44 | |
| 14 | 115 | |
| 15 | 0 | |
| 16 | 15 | |
| 17 | 1 | |
| 18 | 117 |
About Thomas Clozel
Thomas Clozel is a scholar working on Health Informatics, Oncology and Biophysics, having authored 18 papers that have together received 1.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Bladder and Urothelial Cancer Treatments (4 papers) and AI in cancer detection (4 papers). The work is most often cited by research in Health Informatics (75 citations), Radiology, Nuclear Medicine and Imaging (463 citations) and Biophysics (102 citations). Thomas Clozel has collaborated with scholars based in United States, France and Canada. Frequent co-authors include Gilles Wainrib, Pierre Courtiol, Matahi Moarii, Mikhail Zaslavskiy, Elodie Pronier, Olivier Elemento, Meriem Sefta, Charlie Saillard, Benoît Schmauch and Julien Caldéraro. Their work appears in journals such as Nature Medicine, Nature Communications and Journal of Clinical 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.