Norman Zerbe

1.6k total citations
34 papers, 780 citations indexed

About

Norman Zerbe is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Norman Zerbe has authored 34 papers receiving a total of 780 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 14 papers in Radiology, Nuclear Medicine and Imaging and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Norman Zerbe's work include AI in cancer detection (25 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Artificial Intelligence in Healthcare and Education (8 papers). Norman Zerbe is often cited by papers focused on AI in cancer detection (25 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Artificial Intelligence in Healthcare and Education (8 papers). Norman Zerbe collaborates with scholars based in Germany, Italy and France. Norman Zerbe's co-authors include Peter Hufnagl, Harshita Sharma, Olaf Hellwich, Tim‐Rasmus Kiehl, Christian Geißler, Markus Plass, Andreas Holzinger, Michaela Kargl, Heimo Müller and Matthias Endres and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Stroke.

In The Last Decade

Norman Zerbe

28 papers receiving 761 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Norman Zerbe Germany 12 501 314 194 118 103 34 780
Alexi Baidoshvili Netherlands 15 492 1.0× 346 1.1× 193 1.0× 149 1.3× 43 0.4× 22 920
Iringo Kovacs Netherlands 5 538 1.1× 386 1.2× 181 0.9× 105 0.9× 61 0.6× 7 762
Hans Pinckaers Netherlands 12 608 1.2× 477 1.5× 183 0.9× 150 1.3× 88 0.9× 20 899
Thomas de Bel Netherlands 10 641 1.3× 459 1.5× 231 1.2× 159 1.3× 103 1.0× 16 1.0k
Niels Olson United States 6 467 0.9× 370 1.2× 99 0.5× 83 0.7× 137 1.3× 9 734
Péter Bándi Netherlands 8 617 1.2× 421 1.3× 292 1.5× 116 1.0× 43 0.4× 15 779
Chengkuan Chen United States 4 488 1.0× 372 1.2× 177 0.9× 81 0.7× 120 1.2× 5 807
N. K. Timofeeva Netherlands 4 717 1.4× 471 1.5× 292 1.5× 137 1.2× 65 0.6× 10 963
Wouter Bulten Netherlands 8 752 1.5× 530 1.7× 275 1.4× 157 1.3× 94 0.9× 9 998
André Homeyer Germany 13 376 0.8× 294 0.9× 229 1.2× 91 0.8× 40 0.4× 37 835

Countries citing papers authored by Norman Zerbe

Since Specialization
Citations

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

Fields of papers citing papers by Norman Zerbe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Norman Zerbe

This figure shows the co-authorship network connecting the top 25 collaborators of Norman Zerbe. A scholar is included among the top collaborators of Norman Zerbe 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 Norman Zerbe. Norman Zerbe 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.
Plass, Markus, Peter Regitnig, Kristijan Skok, et al.. (2026). From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology. Artificial Intelligence in Medicine. 174. 103368–103368.
2.
Philbrick, Kenneth A., et al.. (2025). Maintaining and broadening DICOM adoption in digital pathology: A response to “wearing a fur coat in the summertime”. Journal of Pathology Informatics. 19. 100517–100517.
3.
Deman, Frederik, Glenn Broeckx, Inti Zlobec, et al.. (2025). Practical consequences of the European union-AI act for anatomic pathology laboratories a European society of pathology and European society of digital and integrative pathology commissioned expert opinion paper. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin.
4.
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Montezuma, Diana, Rouven Porz, Vincenzo L’Imperio, et al.. (2025). Unbiased Artificial Intelligence: Addressing Bias in Computational Pathology. PubMed. 3(4). 100302–100302.
6.
Zander, Thomas, V. Barroso, Norman Zerbe, et al.. (2025). AI-based tumor-stroma ratio quantification algorithm: comprehensive evaluation of prognostic role in primary colorectal cancer. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 487(4). 799–813. 1 indexed citations
7.
Montezuma, Diana, Yuri Tolkach, Peter Boor, et al.. (2024). Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study. Laboratory Investigation. 105(3). 102203–102203. 1 indexed citations
8.
Zerbe, Norman, Lars Ole Schwen, Christian Geißler, et al.. (2024). Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative. Journal of Pathology Informatics. 15. 100387–100387. 8 indexed citations
9.
Plass, Markus, Michaela Kargl, Tim‐Rasmus Kiehl, et al.. (2023). Explainability and causability in digital pathology. The Journal of Pathology Clinical Research. 9(4). 251–260. 45 indexed citations
10.
Hassell, Lewis, Tim‐Rasmus Kiehl, Norman Zerbe, et al.. (2023). Evolving educational landscape in pathology: a comprehensive bibliometric and visual analysis including digital teaching and learning resources. Journal of Clinical Pathology. 77(2). 87–95. 5 indexed citations
11.
Franz, Michael, et al.. (2023). Anonymization of whole slide images in histopathology for research and education. Digital Health. 9. 589815187–589815187. 6 indexed citations
12.
Fraggetta, Filippo, Vincenzo L’Imperio, Sabine Leh, et al.. (2021). Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics. 11(11). 2167–2167. 69 indexed citations
13.
Homeyer, André, Johannes Lotz, Lars Ole Schwen, et al.. (2021). Artificial Intelligence in Pathology: From Prototype to Product. Journal of Pathology Informatics. 12(1). 13–13. 23 indexed citations
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Euskirchen, Philipp, Josefine Radke, Ulrike Grittner, et al.. (2017). Cellular heterogeneity contributes to subtype-specific expression of ZEB1 in human glioblastoma. PLoS ONE. 12(9). e0185376–e0185376. 8 indexed citations
17.
Sharma, Harshita, et al.. (2017). Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Computerized Medical Imaging and Graphics. 61. 2–13. 233 indexed citations
18.
Mate, Sebastian, Raphael W. Majeed, Holger Storf, et al.. (2017). Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network. Studies in health technology and informatics. 243. 100–104. 10 indexed citations
19.
Hufnagl, Peter, Norman Zerbe, & Karsten Schlüns. (2012). Virtuelle Mikroskopie in der onkologischen Diagnostik. Der Onkologe. 18(5). 409–418. 1 indexed citations
20.
Zerbe, Norman, Peter Hufnagl, & Karsten Schlüns. (2011). Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images. Diagnostic Pathology. 6(S1). S16–S16. 29 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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