Marko van Treeck
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 7
- Topic Modeling 2
- Machine Learning in Healthcare 2
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- Radiomics and Machine Learning in Medical Imaging 9
- Co-authors
- Jakob Nikolas Kather (21 shared papers)Gregory Patrick Veldhuizen (7 shared papers)Daniel Truhn (7 shared papers)Katherine Hewitt (5 shared papers)Peter Boor (3 shared papers)Chiara Maria Lavinia Loeffler (6 shared papers)Oliver Lester Saldanha (7 shared papers)Sebastian Foersch (4 shared papers)
- Journals
- npj Precision Oncology (3 papers)Nature Communications (2 papers)International Journal of Surgery (1 paper)BMC Biology (1 paper)Scientific Reports (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Marko van Treeck
16 papers receiving 342 citations
Peers
Comparison fields: 5 of 70
- Health Informatics 50
- Radiology, Nuclear Medicine and Imaging 178
- Artificial Intelligence 209
- Biophysics 28
- Cancer Research 60
Countries citing papers authored by Marko van Treeck
This map shows the geographic impact of Marko van Treeck'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 Marko van Treeck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marko van Treeck more than expected).
Fields of papers citing papers by Marko van Treeck
This network shows the impact of papers produced by Marko van Treeck. 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 Marko van Treeck. The network helps show where Marko van Treeck may publish in the future.
Co-authors
The 25 scholars most cited alongside Marko van Treeck, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 53 | |
| 2 | 2023 | 46 | |
| 3 | 2023 | 41 | |
| 4 | 2024 | 35 | |
| 5 | 2024 | 30 | |
| 6 | 2024 | 27 | |
| 7 | 2022 | 24 | |
| 8 | 2023 | 21 | |
| 9 | 2022 | 17 | |
| 10 | 2023 | 13 | |
| 11 | 2025 | 10 | |
| 12 | 2024 | 10 | |
| 13 | 2022 | 7 | |
| 14 | 2024 | 4 | |
| 15 | 2025 | 4 | |
| 16 | 2023 | 3 | |
| 17 | 2025 | 0 | |
| 18 | 2025 | 0 | |
| 19 | 2025 | 0 | |
| 20 | 2025 | 0 |
About Marko van Treeck
Marko van Treeck is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Oncology, Cancer Research and Molecular Biology, having authored 21 papers that have together received 345 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers), Cancer Genomics and Diagnostics (6 papers), Colorectal Cancer Screening and Detection (5 papers), Artificial Intelligence in Healthcare and Education (3 papers), Topic Modeling (2 papers), Machine Learning in Healthcare (2 papers) and Hepatocellular Carcinoma Treatment and Prognosis (1 paper). The work is most often cited by research in Health Informatics (50 citations), Radiology, Nuclear Medicine and Imaging (178 citations), Artificial Intelligence (209 citations), Biophysics (28 citations) and Cancer Research (60 citations). Marko van Treeck has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Jakob Nikolas Kather, Gregory Patrick Veldhuizen, Daniel Truhn, Katherine Hewitt, Peter Boor, Chiara Maria Lavinia Loeffler, Oliver Lester Saldanha, Sebastian Foersch, Narmin Ghaffari Laleh and Bastian Dislich. Their work appears in journals such as npj Precision Oncology, Nature Communications, International Journal of Surgery, BMC Biology 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.