Tabea Kossen
Impact in
- Health Informatics top 10%
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
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- Acute Ischemic Stroke Management 6
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- Radiomics and Machine Learning in Medical Imaging 3
- COVID-19 diagnosis using AI 2
- Co-authors
- Dietmar Frey (9 shared papers)Vince I. Madai (10 shared papers)Jan Sobesky (8 shared papers)Michelle Livne (4 shared papers)Kristian Hildebrand (5 shared papers)Abdel Aziz Taha (1 shared paper)John D. Kelleher (1 shared paper)Jana Rieger (1 shared paper)
- Journals
- Frontiers in Neurology (2 papers)Computers in Biology and Medicine (1 paper)Cancers (1 paper)Neurosurgical Review (1 paper)NeuroImage (1 paper)
- Partner nations
- GermanyUnited KingdomNetherlands
In The Last Decade
Tabea Kossen
12 papers receiving 307 citations
Peers
Comparison fields: 5 of 65
- Health Informatics 15
- Computer Vision and Pattern Recognition 137
- Neurology 55
- Radiology, Nuclear Medicine and Imaging 119
- Pulmonary and Respiratory Medicine 73
Countries citing papers authored by Tabea Kossen
This map shows the geographic impact of Tabea Kossen'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 Tabea Kossen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tabea Kossen more than expected).
Fields of papers citing papers by Tabea Kossen
This network shows the impact of papers produced by Tabea Kossen. 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 Tabea Kossen. The network helps show where Tabea Kossen may publish in the future.
Co-authors
The 25 scholars most cited alongside Tabea Kossen, 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 | 2019 | 166 | |
| 2 | 2021 | 44 | |
| 3 | 2022 | 29 | |
| 4 | 2024 | 26 | |
| 5 | 2021 | 10 | |
| 6 | 2023 | 8 | |
| 7 | 2022 | 8 | |
| 8 | 2022 | 7 | |
| 9 | 2017 | 5 | |
| 10 | 2023 | 4 | |
| 11 | 2024 | 2 | |
| 12 | 2023 | 1 |
About Tabea Kossen
Tabea Kossen is a scholar working on Epidemiology, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 12 papers that have together received 310 indexed citations. Recurring topics across this work include Acute Ischemic Stroke Management (6 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), COVID-19 diagnosis using AI (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), AI in cancer detection (2 papers), Cerebrovascular and Carotid Artery Diseases (2 papers) and Stroke Rehabilitation and Recovery (1 paper). The work is most often cited by research in Health Informatics (15 citations), Computer Vision and Pattern Recognition (137 citations), Neurology (55 citations), Radiology, Nuclear Medicine and Imaging (119 citations) and Pulmonary and Respiratory Medicine (73 citations). Tabea Kossen has collaborated with scholars based in Germany, United Kingdom and Netherlands. Frequent co-authors include Dietmar Frey, Vince I. Madai, Jan Sobesky, Michelle Livne, Kristian Hildebrand, Abdel Aziz Taha, John D. Kelleher, Jana Rieger, Orhun Utku Aydin and Ela M. Akay. Their work appears in journals such as Frontiers in Neurology, Computers in Biology and Medicine, Cancers, Neurosurgical Review and NeuroImage.
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.