Gregor Koehler
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Neurology top 10%
- Brain Tumor Detection and Classification
Papers in
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- Topic Modeling 1
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- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Klaus Maier‐Hein (6 shared papers)Fabian Isensee (4 shared papers)Jens Petersen (2 shared papers)André Klein (1 shared paper)David Zimmerer (3 shared papers)Simon Köhl (2 shared papers)Tobias Norajitra (1 shared paper)Sebastian Wirkert (1 shared paper)
- Journals
- Production Engineering (1 paper)Lecture notes in computer science (2 papers)JMIR Medical Informatics (1 paper)FreiDok plus (Universitätsbibliothek Freiburg) (2 papers)Informatik aktuell (2 papers)
- Partner nations
- GermanyUnited States
In The Last Decade
Gregor Koehler
7 papers receiving 556 citations
Gregor Koehler's Hit Papers
Peers
Comparison fields: 5 of 80
- Computer Vision and Pattern Recognition 280
- Neurology 90
- Radiology, Nuclear Medicine and Imaging 217
- Health Informatics 13
- Artificial Intelligence 160
Countries citing papers authored by Gregor Koehler
This map shows the geographic impact of Gregor Koehler'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 Gregor Koehler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregor Koehler more than expected).
Fields of papers citing papers by Gregor Koehler
This network shows the impact of papers produced by Gregor Koehler. 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 Gregor Koehler. The network helps show where Gregor Koehler may publish in the future.
Co-authors
The 23 scholars most cited alongside Gregor Koehler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation Hit paper breakdown → | 2019 | 413 |
| 2 | MedNeXt: Transformer-Driven Scaling of ConvNets for Medical Image Segmentation Hit paper breakdown → | 2023 | 137 |
| 3 | 2020 | 16 | |
| 4 | 2010 | 3 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 1 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 0 |
About Gregor Koehler
Gregor Koehler is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Control and Systems Engineering, having authored 8 papers that have together received 573 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), COVID-19 diagnosis using AI (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Metallurgy and Material Forming (1 paper), Medical Image Segmentation Techniques (1 paper), Brain Tumor Detection and Classification (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (280 citations), Neurology (90 citations), Radiology, Nuclear Medicine and Imaging (217 citations), Health Informatics (13 citations) and Artificial Intelligence (160 citations). Gregor Koehler has collaborated with scholars based in Germany and United States. Frequent co-authors include Klaus Maier‐Hein, Fabian Isensee, Jens Petersen, André Klein, David Zimmerer, Simon Köhl, Tobias Norajitra, Sebastian Wirkert, Paul F. Jaeger and Jakob Wasserthal. Their work appears in journals such as Production Engineering, Lecture notes in computer science, JMIR Medical Informatics, FreiDok plus (Universitätsbibliothek Freiburg) and Informatik aktuell.
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.