Fabian Eitel
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Brain Tumor Detection and Classification
Papers in
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- Machine Learning in Healthcare 4
- AI in cancer detection 2
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- Functional Brain Connectivity Studies 4
- Co-authors
- Kerstin Ritter (9 shared papers)Martin Weygandt (5 shared papers)Moritz Böhle (2 shared papers)Marc-André Schulz (2 shared papers)Henrik Walter (1 shared paper)Friedemann Paul (4 shared papers)Tanja Schmitz‐Hübsch (3 shared papers)Mohamad Habes (1 shared paper)
- Journals
- Experimental Neurology (1 paper)iScience (1 paper)Scientific Reports (1 paper)Frontiers in Aging Neuroscience (1 paper)Alzheimer s Research & Therapy (1 paper)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Fabian Eitel
9 papers receiving 226 citations
Peers
Comparison fields: 5 of 76
- Health Informatics 40
- Neurology 37
- Artificial Intelligence 114
- Health Information Management 15
- Cognitive Neuroscience 48
Countries citing papers authored by Fabian Eitel
This map shows the geographic impact of Fabian Eitel'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 Fabian Eitel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabian Eitel more than expected).
Fields of papers citing papers by Fabian Eitel
This network shows the impact of papers produced by Fabian Eitel. 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 Fabian Eitel. The network helps show where Fabian Eitel may publish in the future.
Co-authors
The 18 scholars most cited alongside Fabian Eitel, 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 | 162 | |
| 2 | 2021 | 32 | |
| 3 | 2023 | 13 | |
| 4 | 2022 | 6 | |
| 5 | 2023 | 5 | |
| 6 | 2021 | 4 | |
| 7 | 2024 | 4 | |
| 8 | 2020 | 3 | |
| 9 | Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data | 2019 | 2 |
About Fabian Eitel
Fabian Eitel is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Behavioral Neuroscience, having authored 9 papers that have together received 231 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (4 papers), Machine Learning in Healthcare (4 papers), Advanced Neuroimaging Techniques and Applications (3 papers), AI in cancer detection (2 papers), Multiple Sclerosis Research Studies (2 papers), Stress Responses and Cortisol (2 papers), Dementia and Cognitive Impairment Research (2 papers) and Long-Term Effects of COVID-19 (1 paper). The work is most often cited by research in Health Informatics (40 citations), Neurology (37 citations), Artificial Intelligence (114 citations), Health Information Management (15 citations) and Cognitive Neuroscience (48 citations). Fabian Eitel has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Kerstin Ritter, Martin Weygandt, Moritz Böhle, Marc-André Schulz, Henrik Walter, Friedemann Paul, Tanja Schmitz‐Hübsch, Mohamad Habes, Claudia Chien and Judith Bellmann–Strobl. Their work appears in journals such as Experimental Neurology, iScience, Scientific Reports, Frontiers in Aging Neuroscience and Alzheimer s Research & Therapy.
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.