Thomas Thywissen
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Oncology top 10%
- Colorectal Cancer Surgical Treatments
- Colorectal Cancer Screening and Detection
Papers in
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- Radiomics and Machine Learning in Medical Imaging 4
- MRI in cancer diagnosis 2
- Medical Imaging Techniques and Applications 1
- Oncology 3
- Colorectal Cancer Surgical Treatments 2
- Co-authors
- Geerard L. Beets (4 shared papers)Monique Maas (3 shared papers)Doenja M. J. Lambregts (4 shared papers)Luís Curvo‐Semedo (2 shared papers)Alfons G.H. Kessels (2 shared papers)Filipe Caseiro‐Alves (1 shared paper)Guido Lammering (1 shared paper)Anke Meyer‐Baese (1 shared paper)
- Journals
- European Journal of Radiology (1 paper)Radiology (1 paper)Acta Radiologica (1 paper)European Radiology (1 paper)European Journal of Cancer (1 paper)
- Partner nations
- NetherlandsUnited StatesGermany
In The Last Decade
Thomas Thywissen
5 papers receiving 493 citations
Peers
Comparison fields: 5 of 32
- Radiology, Nuclear Medicine and Imaging 410
- Oncology 278
- Hepatology 28
- Obstetrics and Gynecology 27
- Pulmonary and Respiratory Medicine 92
Countries citing papers authored by Thomas Thywissen
This map shows the geographic impact of Thomas Thywissen'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 Thywissen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Thywissen more than expected).
Fields of papers citing papers by Thomas Thywissen
This network shows the impact of papers produced by Thomas Thywissen. 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 Thywissen. The network helps show where Thomas Thywissen may publish in the future.
Co-authors
The 20 scholars most cited alongside Thomas Thywissen, 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 | 2011 | 208 | |
| 2 | 2011 | 206 | |
| 3 | 2019 | 47 | |
| 4 | 2017 | 33 | |
| 5 | 2017 | 5 |
About Thomas Thywissen
Thomas Thywissen is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology, Biomedical Engineering, Pulmonary and Respiratory Medicine and Artificial Intelligence, having authored 5 papers that have together received 499 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Colorectal Cancer Surgical Treatments (2 papers), MRI in cancer diagnosis (2 papers), Advanced X-ray and CT Imaging (2 papers), Hepatocellular Carcinoma Treatment and Prognosis (1 paper), AI in cancer detection (1 paper), Medical Imaging Techniques and Applications (1 paper) and Digital Radiography and Breast Imaging (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (410 citations), Oncology (278 citations), Hepatology (28 citations), Obstetrics and Gynecology (27 citations) and Pulmonary and Respiratory Medicine (92 citations). Thomas Thywissen has collaborated with scholars based in Netherlands, United States and Germany. Frequent co-authors include Geerard L. Beets, Monique Maas, Doenja M. J. Lambregts, Luís Curvo‐Semedo, Alfons G.H. Kessels, Filipe Caseiro‐Alves, Guido Lammering, Anke Meyer‐Baese, Ivo Houben and Joachim E. Wildberger. Their work appears in journals such as European Journal of Radiology, Radiology, Acta Radiologica, European Radiology and European Journal of Cancer.
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.