Tim Rädsch
Impact in
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- Artificial Intelligence in Healthcare and Education
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- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
Papers in
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- Anomaly Detection Techniques and Applications 2
- Machine Learning and Data Classification 1
- AI in cancer detection 1
- Data Stream Mining Techniques 1
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- Artificial Intelligence in Healthcare and Education 2
- Co-authors
- Annette Kopp‐Schneider (2 shared papers)Lena Maier‐Hein (2 shared papers)Vivienn Weru (2 shared papers)Minu D. Tizabi (2 shared papers)Annika Reinke (2 shared papers)Nicholas Schreck (1 shared paper)Ali Emre Kavur (1 shared paper)Tobias Roß (1 shared paper)
- Journals
- Journal of the Association for Information Systems (2 papers)Nature Machine Intelligence (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- GermanyUnited StatesNew Zealand
In The Last Decade
Tim Rädsch
4 papers receiving 57 citations
Peers
Comparison fields: 5 of 40
- Health Informatics 9
- Biophysics 6
- Radiology, Nuclear Medicine and Imaging 16
- Artificial Intelligence 27
- Ecological Modeling 2
Countries citing papers authored by Tim Rädsch
This map shows the geographic impact of Tim Rädsch'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 Tim Rädsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Rädsch more than expected).
Fields of papers citing papers by Tim Rädsch
This network shows the impact of papers produced by Tim Rädsch. 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 Tim Rädsch. The network helps show where Tim Rädsch may publish in the future.
Co-authors
The 13 scholars most cited alongside Tim Rädsch, 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 | 2023 | 43 | |
| 2 | 2021 | 10 | |
| 3 | 2024 | 3 | |
| 4 | 2021 | 1 |
About Tim Rädsch
Tim Rädsch is a scholar working on Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 4 papers that have together received 57 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Anomaly Detection Techniques and Applications (2 papers), Machine Learning and Data Classification (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper), AI in cancer detection (1 paper), Advanced Neural Network Applications (1 paper) and Data Stream Mining Techniques (1 paper). The work is most often cited by research in Health Informatics (9 citations), Biophysics (6 citations), Radiology, Nuclear Medicine and Imaging (16 citations), Artificial Intelligence (27 citations) and Ecological Modeling (2 citations). Tim Rädsch has collaborated with scholars based in Germany, United States and New Zealand. Frequent co-authors include Annette Kopp‐Schneider, Lena Maier‐Hein, Vivienn Weru, Minu D. Tizabi, Annika Reinke, Nicholas Schreck, Ali Emre Kavur, Tobias Roß, Scott Thiebes and Ali Sunyaev. Their work appears in journals such as Journal of the Association for Information Systems, Nature Machine Intelligence and Lecture notes in computer science.
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.