Markus Plass

1.5k total citations
24 papers, 702 citations indexed

About

Markus Plass is a scholar working on Artificial Intelligence, Health Informatics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Markus Plass has authored 24 papers receiving a total of 702 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Health Informatics and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Markus Plass's work include Radiomics and Machine Learning in Medical Imaging (8 papers), Artificial Intelligence in Healthcare and Education (8 papers) and AI in cancer detection (7 papers). Markus Plass is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), Artificial Intelligence in Healthcare and Education (8 papers) and AI in cancer detection (7 papers). Markus Plass collaborates with scholars based in Austria, Germany and Italy. Markus Plass's co-authors include Stephan Jahn, Farid Moinfar, Andreas Holzinger, Heimo Müller, Michaela Kargl, Katharina Holzinger, Gloria Cerasela Crişan, Vasile Palade, Michael Kickmeier-Rust and Camelia-M. Pintea and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Cancer Research.

In The Last Decade

Markus Plass

22 papers receiving 672 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Markus Plass Austria 11 407 187 148 85 71 24 702
Ehsan Kazemi United States 13 438 1.1× 174 0.9× 59 0.4× 147 1.7× 43 0.6× 24 991
Claudia Mazo Ireland 9 332 0.8× 159 0.9× 170 1.1× 48 0.6× 49 0.7× 21 624
Ellery Wulczyn United States 9 746 1.8× 357 1.9× 74 0.5× 92 1.1× 89 1.3× 16 986
Sarthak Pati United States 11 585 1.4× 443 2.4× 166 1.1× 96 1.1× 45 0.6× 27 1.1k
Jason Martin United States 7 765 1.9× 333 1.8× 208 1.4× 96 1.1× 34 0.5× 12 1.1k
Davis Foote United States 2 314 0.8× 169 0.9× 52 0.4× 62 0.7× 33 0.5× 2 419
Micah Sheller United States 5 749 1.8× 338 1.8× 215 1.5× 78 0.9× 35 0.5× 7 1.1k
Brandon Edwards United States 7 758 1.9× 338 1.8× 215 1.5× 80 0.9× 35 0.5× 10 1.1k
Hong‐Jun Yoon United States 16 586 1.4× 244 1.3× 55 0.4× 75 0.9× 85 1.2× 68 1.0k
Mohsin Bilal United Kingdom 12 283 0.7× 206 1.1× 24 0.2× 146 1.7× 116 1.6× 33 557

Countries citing papers authored by Markus Plass

Since Specialization
Citations

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

Fields of papers citing papers by Markus Plass

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Markus Plass

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Plass. A scholar is included among the top collaborators of Markus Plass 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 Markus Plass. Markus Plass 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.
Plass, Markus, Sanja Đačić, Izidor Kern, et al.. (2025). Comparative performance of PD‐L1 scoring by pathologists and AI algorithms. Histopathology. 87(1). 90–100. 3 indexed citations
3.
Grosser, Bianca, David F. Steiner, Veselin Grozdanov, et al.. (2024). Converging deep learning and human-observed tumor-adipocyte interaction as a biomarker in colorectal cancer. SHILAP Revista de lepidopterología. 4(1). 163–163. 7 indexed citations
4.
Plass, Markus, et al.. (2024). Fine-tuning language model embeddings to reveal domain knowledge: An explainable artificial intelligence perspective on medical decision making. Engineering Applications of Artificial Intelligence. 139. 109561–109561. 17 indexed citations
5.
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
6.
Holub, Petr, Heimo Müller, Luca Pireddu, et al.. (2023). Privacy risks of whole-slide image sharing in digital pathology. Nature Communications. 14(1). 2577–2577. 14 indexed citations
7.
Krogue, Justin D., Shekoofeh Azizi, Fraser Elisabeth Tan, et al.. (2023). Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. SHILAP Revista de lepidopterología. 3(1). 59–59. 12 indexed citations
8.
Plass, Markus, et al.. (2023). Provenance of specimen and data – A prerequisite for AI development in computational pathology. New Biotechnology. 78. 22–28. 4 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.
Höhn, Julia, Eva Krieghoff‐Henning, Jitendra Jonnagaddala, et al.. (2023). Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning. npj Precision Oncology. 7(1). 98–98. 5 indexed citations
11.
L’Imperio, Vincenzo, Ellery Wulczyn, Markus Plass, et al.. (2023). Pathologist Validation of a Machine Learning–Derived Feature for Colon Cancer Risk Stratification. JAMA Network Open. 6(3). e2254891–e2254891. 31 indexed citations
12.
Müller, Heimo, Petr Holub, Markus Plass, et al.. (2023). BIBBOX, a FAIR toolbox and App Store for life science research. New Biotechnology. 77. 12–19. 2 indexed citations
13.
Müller, Heimo, et al.. (2022). Lightweight Distributed Provenance Model for Complex Real–world Environments. Scientific Data. 9(1). 503–503. 9 indexed citations
14.
Plass, Markus, et al.. (2022). Understanding and Explaining Diagnostic Paths: Toward Augmented Decision Making. IEEE Computer Graphics and Applications. 42(6). 47–57. 9 indexed citations
15.
Müller, Heimo, Andreas Holzinger, Markus Plass, et al.. (2022). Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European In Vitro Diagnostic Regulation. New Biotechnology. 70. 67–72. 52 indexed citations
16.
Retzlaff, Carl Orge, Christian Geißler, Michaela Kargl, et al.. (2022). The explainability paradox: Challenges for xAI in digital pathology. Future Generation Computer Systems. 133. 281–296. 78 indexed citations
17.
Plass, Markus, et al.. (2022). The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes. Studies in health technology and informatics. 294. 415–416. 2 indexed citations
18.
Poppe, Andreas, Luka Brčić, Kurt Zatloukal, et al.. (2021). OpenQKD Use-case for Securing Sensitive Medical Data at rest and in transit. 1–1. 1 indexed citations
19.
Wulczyn, Ellery, David F. Steiner, Melissa Moran, et al.. (2020). Abstract 2096: A deep learning system to predict disease-specific survival in stage II and stage III colorectal cancer. Cancer Research. 80(16_Supplement). 2096–2096. 1 indexed citations
20.
Holzinger, Andreas, Markus Plass, & Michael Kickmeier-Rust. (2016). Interactive Machine Learning (iML): a challenge for Game-based approaches. Neural Information Processing Systems. 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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