C. Lafond
Impact in
- Radiation top 1%
- Advanced Radiotherapy Techniques
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- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
Papers in
- Radiation 46
- Advanced Radiotherapy Techniques 46
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- Medical Imaging Techniques and Applications 16
- Radiomics and Machine Learning in Medical Imaging 9
- Radiation Dose and Imaging 9
- Co-authors
- R. de Crevoisier (45 shared papers)Oscar Acosta (34 shared papers)Antoine Simon (28 shared papers)J. Castelli (24 shared papers)Pascal Haigron (15 shared papers)A. Barateau (25 shared papers)Jean‐Claude Nunes (18 shared papers)B. Rigaud (10 shared papers)
In The Last Decade
C. Lafond
70 papers receiving 1.3k citations
C. Lafond's Hit Papers
Peers
Comparison fields: 5 of 109
- Radiation 633
- Radiology, Nuclear Medicine and Imaging 432
- Otorhinolaryngology 72
- Obstetrics and Gynecology 69
- Pulmonary and Respiratory Medicine 281
Countries citing papers authored by C. Lafond
This map shows the geographic impact of C. Lafond'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 C. Lafond with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Lafond more than expected).
Fields of papers citing papers by C. Lafond
This network shows the impact of papers produced by C. Lafond. 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 C. Lafond. The network helps show where C. Lafond may publish in the future.
Co-authors
The 25 scholars most cited alongside C. Lafond, 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 76 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review Hit paper breakdown → | 2021 | 136 |
| 2 | 2019 | 103 | |
| 3 | 2018 | 93 | |
| 4 | 2001 | 85 | |
| 5 | 2021 | 79 | |
| 6 | 2019 | 67 | |
| 7 | 2013 | 64 | |
| 8 | 2020 | 47 | |
| 9 | 2016 | 44 | |
| 10 | 2018 | 43 | |
| 11 | 2016 | 42 | |
| 12 | 2018 | 36 | |
| 13 | 2013 | 34 | |
| 14 | 2018 | 29 | |
| 15 | 2016 | 24 | |
| 16 | 2016 | 23 | |
| 17 | 2020 | 23 | |
| 18 | 2021 | 22 | |
| 19 | 2017 | 18 | |
| 20 | 1998 | 18 |
About C. Lafond
C. Lafond is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 76 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (46 papers), Medical Imaging Techniques and Applications (16 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), Radiation Dose and Imaging (9 papers), Prostate Cancer Diagnosis and Treatment (8 papers), Head and Neck Cancer Studies (7 papers), Medical Imaging and Analysis (7 papers) and Radiation Therapy and Dosimetry (6 papers). The work is most often cited by research in Radiation (633 citations), Radiology, Nuclear Medicine and Imaging (432 citations), Otorhinolaryngology (72 citations), Obstetrics and Gynecology (69 citations) and Pulmonary and Respiratory Medicine (281 citations). C. Lafond has collaborated with scholars based in France, Australia and Colombia. Frequent co-authors include R. de Crevoisier, Oscar Acosta, Antoine Simon, J. Castelli, Pascal Haigron, A. Barateau, Jean‐Claude Nunes, B. Rigaud, A. Largent and R. de Crevoisier. Their work appears in journals such as Physica Medica, International Journal of Radiation Oncology*Biology*Physics, Physics and Imaging in Radiation Oncology, Frontiers in Oncology and Radiation 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.