David Zimmerer
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- COVID-19 diagnosis using AI
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- Advanced Neural Network Applications
Papers in
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- AI in cancer detection 2
- Anomaly Detection Techniques and Applications 2
- Machine Learning and Data Classification 1
- Neural Networks and Applications 1
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- Video Surveillance and Tracking Methods 1
- Co-authors
- Paul F. Jäger (4 shared papers)Klaus Maier‐Hein (6 shared papers)Fabian Isensee (4 shared papers)Justus Schock (1 shared paper)Peter Neher (1 shared paper)André Klein (1 shared paper)Simon Köhl (3 shared papers)Jakob Wasserthal (1 shared paper)
- Journals
- Computers in Biology and Medicine (1 paper)Medical Image Analysis (1 paper)FreiDok plus (Universitätsbibliothek Freiburg) (1 paper)Zenodo (CERN European Organization for Nuclear Research) (2 papers)
- Partner nations
- GermanyUnited States
In The Last Decade
David Zimmerer
7 papers receiving 54 citations
Peers
Comparison fields: 5 of 28
- Radiology, Nuclear Medicine and Imaging 28
- Computer Vision and Pattern Recognition 23
- Health Informatics 1
- Structural Biology 1
- Artificial Intelligence 20
Countries citing papers authored by David Zimmerer
This map shows the geographic impact of David Zimmerer'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 David Zimmerer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Zimmerer more than expected).
Fields of papers citing papers by David Zimmerer
This network shows the impact of papers produced by David Zimmerer. 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 David Zimmerer. The network helps show where David Zimmerer may publish in the future.
Co-authors
The 25 scholars most cited alongside David Zimmerer, 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 | 2020 | 37 | |
| 2 | 2016 | 6 | |
| 3 | 2020 | 5 | |
| 4 | 2024 | 4 | |
| 5 | 2025 | 1 | |
| 6 | Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection | 2018 | 1 |
| 7 | 2024 | 1 |
About David Zimmerer
David Zimmerer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Automotive Engineering and General Health Professions, having authored 7 papers that have together received 55 indexed citations. Recurring topics across this work include AI in cancer detection (2 papers), COVID-19 diagnosis using AI (2 papers), Anomaly Detection Techniques and Applications (2 papers), Video Surveillance and Tracking Methods (1 paper), Machine Learning and Data Classification (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Autonomous Vehicle Technology and Safety (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (28 citations), Computer Vision and Pattern Recognition (23 citations), Health Informatics (1 citation), Structural Biology (1 citation) and Artificial Intelligence (20 citations). David Zimmerer has collaborated with scholars based in Germany and United States. Frequent co-authors include Paul F. Jäger, Klaus Maier‐Hein, Fabian Isensee, Justus Schock, Peter Neher, André Klein, Simon Köhl, Jakob Wasserthal, Tobias Roß and Sebastian Wirkert. Their work appears in journals such as Computers in Biology and Medicine, Medical Image Analysis, FreiDok plus (Universitätsbibliothek Freiburg) and Zenodo (CERN European Organization for Nuclear Research).
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.