Michael Gadermayr
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Oncology
- Molecular Biology
- Co-authors
- Dorit MerhofGertie Janneke OostinghAndreas UhlPeter BoorBarbara M. KlinkhammerLaxmi GuptaAndreas VécseiBurkhard Gess
- Topics
- AI in cancer detection (14 papers)Colorectal Cancer Screening and Detection (9 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Medical Imaging
- Partner nations
- AustriaGermanySwitzerland
In The Last Decade
Michael Gadermayr
36 papers receiving 450 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 234
- Computer Vision and Pattern Recognition 159
- Radiology, Nuclear Medicine and Imaging 149
- Oncology 55
- Molecular Biology 43
Countries citing papers authored by Michael Gadermayr
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 40 | |
| 3 | 3 | |
| 4 | 9 | |
| 5 | 8 | |
| 6 | 4 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | 73 | |
| 11 | 69 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 13 | |
| 15 | 22 | |
| 16 | 26 | |
| 17 | 2 | |
| 18 | 4 | |
| 19 | 5 | |
| 20 | 13 |
About Michael Gadermayr
Michael Gadermayr is a scholar working on Gastroenterology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 37 papers that have together received 461 indexed citations. Recurring topics across this 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). The work is most often cited by research in Health Informatics (26 citations), Computer Vision and Pattern Recognition (159 citations) and Gastroenterology (42 citations). Michael Gadermayr has collaborated with scholars based in Austria, Germany and Switzerland. Frequent 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. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Medical Imaging.
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.