Marko van Treeck

1.1k total citations
21 papers, 345 citations indexed

About

Marko van Treeck is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Marko van Treeck has authored 21 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 11 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Oncology. Recurrent topics in Marko van Treeck's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers) and Cancer Genomics and Diagnostics (6 papers). Marko van Treeck is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers) and Cancer Genomics and Diagnostics (6 papers). Marko van Treeck collaborates with scholars based in Germany, United Kingdom and United States. Marko van Treeck's co-authors include Jakob Nikolas Kather, Daniel Truhn, Gregory Patrick Veldhuizen, Katherine Hewitt, Peter Boor, Chiara Maria Lavinia Loeffler, Rupert Langer, Sebastian Foersch, Bastian Dislich and Oliver Lester Saldanha and has published in prestigious journals such as Nature Communications, Scientific Reports and Nature Protocols.

In The Last Decade

Marko van Treeck

16 papers receiving 342 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marko van Treeck Germany 11 209 178 64 60 50 21 345
Ronnachai Jaroensri United States 5 200 1.0× 151 0.8× 57 0.9× 43 0.7× 54 1.1× 7 328
Emmanuel Agosto‐Arroyo United States 5 207 1.0× 148 0.8× 81 1.3× 57 0.9× 48 1.0× 11 327
Peter Truszkowski United States 2 211 1.0× 187 1.1× 60 0.9× 41 0.7× 66 1.3× 2 341
Charles Maussion France 4 190 0.9× 178 1.0× 67 1.0× 57 0.9× 34 0.7× 12 338
Andrew Lagree Canada 11 151 0.7× 192 1.1× 52 0.8× 61 1.0× 25 0.5× 15 295
Ivy Liang United States 2 228 1.1× 161 0.9× 38 0.6× 34 0.6× 72 1.4× 3 385
Asmaa Ibrahim Egypt 9 156 0.7× 139 0.8× 79 1.2× 84 1.4× 30 0.6× 22 270
Vipul Baxi United States 6 206 1.0× 205 1.2× 112 1.8× 73 1.2× 71 1.4× 16 446
Luca L. Weishaupt United States 3 288 1.4× 225 1.3× 61 1.0× 50 0.8× 83 1.7× 6 499
Lorraine Corsale United States 6 237 1.1× 124 0.7× 64 1.0× 30 0.5× 35 0.7× 6 301

Countries citing papers authored by Marko van Treeck

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Marko van Treeck

This figure shows the co-authorship network connecting the top 25 collaborators of Marko van Treeck. A scholar is included among the top collaborators of Marko van Treeck 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 Marko van Treeck. Marko van Treeck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wiest, Isabella C., F M Wolf, Dyke Ferber, et al.. (2025). Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer. NEJM AI. 2(4). 10 indexed citations
2.
Veldhuizen, Gregory Patrick, Didem Çifçi, Marko van Treeck, et al.. (2025). Deep learning can predict cardiovascular events from liver imaging. JHEP Reports. 7(8). 101427–101427.
3.
Treeck, Marko van, Yvonne Döerffel, Jens Berger, et al.. (2025). Weakly Supervised Deep Learning Can Analyze Focal Liver Lesions in Contrast-Enhanced Ultrasound. Digestion. 106(6). 502–514.
4.
Kolbinger, Fiona R., Oliver Lester Saldanha, Steffen Pistorius, et al.. (2025). Vision-language models for automated video analysis and documentation in laparoscopic surgery: a proof-of-concept study. International Journal of Surgery. 111(11). 7777–7786.
5.
6.
Nahhas, Omar S. M. El, Hannah Sophie Muti, Michael Hoffmeister, et al.. (2025). Benchmarking foundation models as feature extractors for weakly supervised computational pathology. Nature Biomedical Engineering. 4 indexed citations
7.
Carrero, Zunamys I., Chiara Maria Lavinia Loeffler, Marko van Treeck, et al.. (2024). Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology. npj Precision Oncology. 8(1). 115–115. 10 indexed citations
8.
Nahhas, Omar S. M. El, Chiara Maria Lavinia Loeffler, Zunamys I. Carrero, et al.. (2024). Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nature Communications. 15(1). 1253–1253. 35 indexed citations
9.
Loeffler, Chiara Maria Lavinia, Omar S. M. El Nahhas, Zunamys I. Carrero, et al.. (2024). Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types. BMC Biology. 22(1). 225–225. 4 indexed citations
10.
Nahhas, Omar S. M. El, Marko van Treeck, Georg Wölflein, et al.. (2024). From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nature Protocols. 20(1). 293–316. 27 indexed citations
11.
Wiest, Isabella C., Dyke Ferber, Marko van Treeck, et al.. (2024). Privacy-preserving large language models for structured medical information retrieval. npj Digital Medicine. 7(1). 257–257. 30 indexed citations
12.
Seraphin, Tobias Paul, Mark Luedde, Christoph Roderburg, et al.. (2023). Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning. European Heart Journal - Digital Health. 4(3). 265–274. 21 indexed citations
13.
Saldanha, Oliver Lester, Chiara Maria Lavinia Loeffler, Jan Niehues, et al.. (2023). Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. npj Precision Oncology. 7(1). 35–35. 41 indexed citations
14.
Niehues, Jan, Philip Quirke, Nicholas P. West, et al.. (2023). Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study. Cell Reports Medicine. 4(4). 100980–100980. 46 indexed citations
15.
Hewitt, Katherine, Chiara Maria Lavinia Löffler, Hannah Sophie Muti, et al.. (2023). Direct image to subtype prediction for brain tumors using deep learning. Neuro-Oncology Advances. 5(1). vdad139–vdad139. 13 indexed citations
16.
Niehues, Jan, Marko van Treeck, Chiara Maria Lavinia Loeffler, et al.. (2023). Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer. npj Breast Cancer. 9(1). 91–91. 3 indexed citations
17.
Laleh, Narmin Ghaffari, Daniel Truhn, Gregory Patrick Veldhuizen, et al.. (2022). Adversarial attacks and adversarial robustness in computational pathology. Nature Communications. 13(1). 5711–5711. 53 indexed citations
18.
Saldanha, Oliver Lester, Hannah Sophie Muti, Heike I. Grabsch, et al.. (2022). Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning. Gastric Cancer. 26(2). 264–274. 24 indexed citations
19.
Loeffler, Chiara Maria Lavinia, Nadine T. Gaisa, Hannah Sophie Muti, et al.. (2022). Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types. Frontiers in Genetics. 12. 806386–806386. 17 indexed citations
20.
Buendgens, Lukas, Didem Çifçi, Narmin Ghaffari Laleh, et al.. (2022). Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy. Scientific Reports. 12(1). 4829–4829. 7 indexed citations

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.

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