Jan van Zelst
Impact in
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Pathology and Forensic Medicine top 10%
- Breast Lesions and Carcinomas
Papers in
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- Breast Lesions and Carcinomas 12
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- AI in cancer detection 10
- Co-authors
- Ritse M. Mann (21 shared papers)Nico Karssemeijer (16 shared papers)Bram Platel (7 shared papers)Roel Mus (3 shared papers)Christian Geppert (1 shared paper)Suzan Vreemann (6 shared papers)Tao Tan (8 shared papers)Albert Gubern‐Mérida (6 shared papers)
- Journals
- Medical Physics (3 papers)Investigative Radiology (3 papers)European Radiology (2 papers)European Journal of Radiology (2 papers)Radiology (1 paper)
- Partner nations
- NetherlandsUnited StatesBelgium
In The Last Decade
Jan van Zelst
21 papers receiving 723 citations
Peers
Comparison fields: 5 of 49
- Radiology, Nuclear Medicine and Imaging 394
- Pathology and Forensic Medicine 137
- Artificial Intelligence 238
- Health Informatics 9
- Pulmonary and Respiratory Medicine 145
Countries citing papers authored by Jan van Zelst
This map shows the geographic impact of Jan van Zelst'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 Jan van Zelst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan van Zelst more than expected).
Fields of papers citing papers by Jan van Zelst
This network shows the impact of papers produced by Jan van Zelst. 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 Jan van Zelst. The network helps show where Jan van Zelst may publish in the future.
Co-authors
The 25 scholars most cited alongside Jan van Zelst, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 165 | |
| 2 | 2018 | 64 | |
| 3 | 2018 | 59 | |
| 4 | 2018 | 56 | |
| 5 | 2017 | 52 | |
| 6 | 2017 | 49 | |
| 7 | 2017 | 45 | |
| 8 | 2015 | 38 | |
| 9 | 2015 | 33 | |
| 10 | 2018 | 30 | |
| 11 | 2014 | 23 | |
| 12 | 2019 | 20 | |
| 13 | 2019 | 18 | |
| 14 | 2016 | 18 | |
| 15 | 2016 | 17 | |
| 16 | 2016 | 15 | |
| 17 | 2017 | 11 | |
| 18 | 2017 | 9 | |
| 19 | 2017 | 8 | |
| 20 | 2020 | 2 |
About Jan van Zelst
Jan van Zelst is a scholar working on Pathology and Forensic Medicine, Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Oncology, having authored 23 papers that have together received 734 indexed citations. Recurring topics across this work include Breast Lesions and Carcinomas (12 papers), AI in cancer detection (10 papers), Digital Radiography and Breast Imaging (7 papers), MRI in cancer diagnosis (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced MRI Techniques and Applications (2 papers), Breast Cancer Treatment Studies (1 paper) and BRCA gene mutations in cancer (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (394 citations), Pathology and Forensic Medicine (137 citations), Artificial Intelligence (238 citations), Health Informatics (9 citations) and Pulmonary and Respiratory Medicine (145 citations). Jan van Zelst has collaborated with scholars based in Netherlands, United States and Belgium. Frequent co-authors include Ritse M. Mann, Nico Karssemeijer, Bram Platel, Roel Mus, Christian Geppert, Suzan Vreemann, Tao Tan, Albert Gubern‐Mérida, Matthieu Rutten and Mathijn de Jong. Their work appears in journals such as Medical Physics, Investigative Radiology, European Radiology, European Journal of Radiology and Radiology.
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.