Christian Matek

1.4k total citations
27 papers, 638 citations indexed

About

Christian Matek is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Christian Matek has authored 27 papers receiving a total of 638 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Artificial Intelligence. Recurrent topics in Christian Matek's work include Digital Imaging for Blood Diseases (7 papers), AI in cancer detection (6 papers) and Cell Image Analysis Techniques (6 papers). Christian Matek is often cited by papers focused on Digital Imaging for Blood Diseases (7 papers), AI in cancer detection (6 papers) and Cell Image Analysis Techniques (6 papers). Christian Matek collaborates with scholars based in Germany, United Kingdom and United States. Christian Matek's co-authors include Carsten Marr, Karsten Spiekermann, Jonathan P. K. Doye, Ard A. Louis, Thomas E. Ouldridge, Torsten Haferlach, Christian Münzenmayer, Lorenzo Rovigatti, Petr Šulc and John S. Schreck and has published in prestigious journals such as Nucleic Acids Research, Nature Medicine and Nature Communications.

In The Last Decade

Christian Matek

21 papers receiving 633 citations

Peers

Christian Matek

CVPR

RNMI

ECOLO

Nicolas Quach United States

Libo Zeng China

Niyun Zhou China

Itay Barnea Israel

Qaiser Chaudry United States

Pavel Matula Czechia

Robin Coope Canada

Hans Netten Netherlands

Yulian A. Menyaev Russia

Nicolas Quach United States

Christian Matek

171 ×0.6

63 ×0.3

CVPR

71 ×0.4

36 ×0.3

RNMI

9 ×0.1

ECOLO

Citations per year, relative to Christian Matek Christian Matek (= 1×) peers Nicolas Quach

Countries citing papers authored by Christian Matek

Since Specialization

Citations

This map shows the geographic impact of Christian Matek'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 Christian Matek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christian Matek more than expected).

Fields of papers citing papers by Christian Matek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christian Matek. 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 Christian Matek. The network helps show where Christian Matek may publish in the future.

Co-authorship network of co-authors of Christian Matek

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

All Works

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20 of 20 papers shown

Kümpers, Christiane, German Ott, Annette Fisseler‐Eckhoff, et al.. (2025). Current practice of pathologic response assessment following chemoimmunotherapy for non‐small cell lung cancer ( NSCLC ) in Germany: first real‐world data from the multicentre Re‐ GraDE study. Histopathology. 87(6). 869–879.

Knöll, Antje, et al.. (2025). Trop2-expression in primary vulvar squamous cell carcinoma. Gynecologic Oncology. 196. 78–84.

Weichert, Wilko, et al.. (2025). Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data. IEEE Transactions on Medical Imaging. 44(5). 2002–2015. 1 indexed citations

Knöll, Antje, et al.. (2025). PD-L1-Expression in primary and recurrent vulvar squamous cell cancer. Gynecologic Oncology. 193. 98–104. 1 indexed citations

Lang‐Schwarz, Corinna, Arndt Hartmann, Michael Vieth, et al.. (2025). Quantitative Analysis of Distinct Colon Crypt Branching Modes Using Interpretable Machine Learning. Inflammatory Bowel Diseases. 31(12). 3407–3416.

Bozorgpour, Afshin, et al.. (2025). Domain-incremental white blood cell classification with privacy-aware continual learning. Scientific Reports. 15(1). 25468–25468. 1 indexed citations

Fischer, David J., Christian Matek, Arndt Hartmann, et al.. (2025). A multimodal dataset for precision oncology in head and neck cancer. Nature Communications. 16(1). 7163–7163.

Kaminski, Wolfgang E., et al.. (2024). Expert-level detection of M-proteins in serum protein electrophoresis using machine learning. Clinical Chemistry and Laboratory Medicine (CCLM). 62(12). 2498–2506. 2 indexed citations

Saillard, Charlie, P Mann, Maxime Touzot, et al.. (2024). AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. Nature Communications. 15(1). 10914–10914. 4 indexed citations

10.

Schulz, Alexander, Isabel N. Schellinger, Sören J. Backhaus, et al.. (2024). Association of Cardiac MRI–derived Aortic Stiffness with Early Stages and Progression of Heart Failure with Preserved Ejection Fraction. Radiology Cardiothoracic Imaging. 6(4). e230344–e230344. 2 indexed citations

11.

Benz, Michaela, Corinna Lang‐Schwarz, Susanne Merkel, et al.. (2023). Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment. Cancers. 15(10). 2675–2675. 9 indexed citations

12.

Lang‐Schwarz, Corinna, et al.. (2023). The Number of Colon Crypts in Digital Mucosal Samples: A New Independent Parameter for Diagnosing Ulcerative Colitis. Cancer Diagnosis & Prognosis. 3(5). 533–537. 2 indexed citations

13.

Wagner, Sophia J., Christian Matek, Melanie Boxberg, et al.. (2023). Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology. Modern Pathology. 37(1). 100350–100350. 11 indexed citations

14.

Wagner, Sophia J., Christian Matek, Melanie Boxberg, et al.. (2022). Make deep learning algorithms in computational pathology more reproducible and reusable. Nature Medicine. 28(9). 1744–1746. 15 indexed citations

15.

Matek, Christian. (2022). More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation. Patterns. 3(1). 100426–100426. 3 indexed citations

16.

Matek, Christian, et al.. (2021). Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 138(20). 1917–1927. 94 indexed citations

17.

Matek, Christian, et al.. (2021). Tens of images can suffice to train neural networks for malignant leukocyte detection. Scientific Reports. 11(1). 7995–7995. 22 indexed citations

18.

Kriegel, Franziska, Christian Matek, Tomáš Dršata, et al.. (2018). The temperature dependence of the helical twist of DNA. Nucleic Acids Research. 46(15). 7998–8009. 49 indexed citations

19.

Sutthibutpong, Thana, Christian Matek, Craig J. Benham, et al.. (2016). Long-range correlations in the mechanics of small DNA circles under topological stress revealed by multi-scale simulation. Nucleic Acids Research. 44(19). gkw815–gkw815. 43 indexed citations

20.

Matek, Christian. (2012). Searching for a conceptual language in Systems Biology: Hints from Statistical Mechanics?. Progress in Biophysics and Molecular Biology. 111(2-3). 116–118. 1 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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