Michael Gadermayr

1.1k total citations
37 papers, 461 citations indexed

About

Michael Gadermayr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Oncology. According to data from OpenAlex, Michael Gadermayr has authored 37 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 9 papers in Oncology. Recurrent topics in Michael Gadermayr's work include AI in cancer detection (14 papers), Colorectal Cancer Screening and Detection (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Michael Gadermayr is often cited by papers focused on AI in cancer detection (14 papers), Colorectal Cancer Screening and Detection (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Michael Gadermayr collaborates with scholars based in Austria, Germany and Switzerland. Michael Gadermayr's co-authors include Dorit Merhof, Gertie Janneke Oostingh, Andreas Uhl, Peter Boor, Barbara M. Klinkhammer, Laxmi Gupta, Andreas Vécsei, Burkhard Gess, Madlaine Müller and Andreas Maier and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Medical Imaging.

In The Last Decade

Michael Gadermayr

36 papers receiving 450 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Gadermayr Austria 11 234 159 149 55 43 37 461
Hidenori Sakanashi Japan 12 309 1.3× 97 0.6× 167 1.1× 119 2.2× 28 0.7× 66 624
Tahir Mahmood South Korea 13 232 1.0× 192 1.2× 342 2.3× 60 1.1× 36 0.8× 30 620
Zhenyuan Ning China 12 289 1.2× 125 0.8× 374 2.5× 45 0.8× 41 1.0× 26 661
Xiaohan Xing China 10 118 0.5× 86 0.5× 131 0.9× 103 1.9× 31 0.7× 22 321
Adrián Colomer Spain 16 323 1.4× 293 1.8× 440 3.0× 114 2.1× 37 0.9× 56 796
Hong‐Ming Tsai Taiwan 11 155 0.7× 177 1.1× 189 1.3× 84 1.5× 15 0.3× 33 648
Caixia Dong China 12 179 0.8× 185 1.2× 118 0.8× 191 3.5× 42 1.0× 32 563
Xiaojing Kang China 9 93 0.4× 100 0.6× 72 0.5× 65 1.2× 35 0.8× 23 317
Yinghao Zhang China 6 141 0.6× 231 1.5× 150 1.0× 30 0.5× 10 0.2× 27 453
Antonio Foncubierta–Rodríguez Switzerland 13 159 0.7× 187 1.2× 233 1.6× 40 0.7× 55 1.3× 29 532

Countries citing papers authored by Michael Gadermayr

Since Specialization
Citations

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

Fields of papers citing papers by Michael Gadermayr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Gadermayr

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Gadermayr. A scholar is included among the top collaborators of Michael Gadermayr 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 Michael Gadermayr. Michael Gadermayr 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.
Sepperer, Thomas, Michael Gadermayr, Markus Himmelsbach, et al.. (2025). Leveraging crude extracts from European tree bark to combat oxidative stress, enhance wound healing, and inhibit pathogenic bacterial growth. Scientific Reports. 15(1). 21340–21340.
2.
Gadermayr, Michael, et al.. (2024). Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential. Computerized Medical Imaging and Graphics. 112. 102337–102337. 40 indexed citations
3.
Gadermayr, Michael, et al.. (2023). Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization. SHILAP Revista de lepidopterología. 3. 100092–100092. 3 indexed citations
4.
Kreutzer, Christina, Peter Boor, Roman D. Bülow, et al.. (2022). On the acceptance of “fake” histopathology: A study on frozen sections optimized with deep learning. Journal of Pathology Informatics. 13. 100168–100168. 9 indexed citations
5.
Gupta, Laxmi, Barbara M. Klinkhammer, Claudia Seikrit, et al.. (2022). Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study. Journal of Pathology Informatics. 13. 100097–100097. 8 indexed citations
6.
Gadermayr, Michael, Kexin Li, Madlaine Müller, et al.. (2021). Image-to-Image Translation for Simplified MRI Muscle Segmentation. PubMed. 1. 664444–664444. 4 indexed citations
7.
Hercher, David, et al.. (2021). Quantification of anomalies in rats’ spinal cords using autoencoders. Computers in Biology and Medicine. 138. 104939–104939. 3 indexed citations
8.
Gadermayr, Michael, Dorit Merhof, Christiane Kühl, et al.. (2021). Automated major psoas muscle volumetry in computed tomography using machine learning algorithms. International Journal of Computer Assisted Radiology and Surgery. 17(2). 355–361. 4 indexed citations
9.
Gess, Burkhard, Madlaine Müller, Sandro Romanzetti, et al.. (2021). Semi-Automatic MRI Muscle Volumetry to Diagnose and Monitor Hereditary and Acquired Polyneuropathies. Brain Sciences. 11(2). 202–202. 1 indexed citations
10.
Oostingh, Gertie Janneke, et al.. (2020). Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential. Patterns. 1(6). 100089–100089. 73 indexed citations
11.
Gadermayr, Michael, et al.. (2019). Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology. IEEE Transactions on Medical Imaging. 38(10). 2293–2302. 69 indexed citations
12.
Wimmer, Georg, Michael Gadermayr, Gernot W. Wolkersdörfer, et al.. (2019). Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging. World Journal of Gastroenterology. 25(10). 1197–1209. 2 indexed citations
13.
Wimmer, Georg, Michael Gadermayr, Roland Kwitt, et al.. (2018). Training of polyp staging systems using mixed imaging modalities. Computers in Biology and Medicine. 102. 251–259. 1 indexed citations
14.
Gadermayr, Michael, Georg Wimmer, Harald Kögler, et al.. (2018). Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis. Computers in Biology and Medicine. 102. 221–226. 13 indexed citations
15.
Gadermayr, Michael, et al.. (2017). Segmenting renal whole slide images virtually without training data. Computers in Biology and Medicine. 90. 88–97. 22 indexed citations
16.
Gadermayr, Michael, et al.. (2017). A comprehensive study on automated muscle segmentation for assessing fat infiltration in neuromuscular diseases. Magnetic Resonance Imaging. 48. 20–26. 26 indexed citations
17.
Gadermayr, Michael & Andreas Uhl. (2016). Making texture descriptors invariant to blur. EURASIP Journal on Image and Video Processing. 2016(1). 14–14. 2 indexed citations
18.
Gadermayr, Michael, Hubert Kogler, Andreas Uhl, & Andreas Vécsei. (2015). Comparing endoscopic imaging configurations in computer-aided celiac disease diagnosis. 163. 446–451. 4 indexed citations
19.
Gadermayr, Michael, Andreas Uhl, & Andreas Vécsei. (2014). Getting one step closer to fully automatized celiac disease diagnosis. 1–5. 5 indexed citations
20.
Gadermayr, Michael, Michael Liedlgruber, Andreas Uhl, & Andreas Vécsei. (2013). Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease. Computer Methods and Programs in Biomedicine. 112(3). 694–712. 13 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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