Moritz Böhle
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- AI in cancer detection
Papers in
-
- Explainable Artificial Intelligence (XAI) 5
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 2
- Adversarial Robustness in Machine Learning 2
- AI in cancer detection 2
-
- Advanced Neural Network Applications 5
- Medical Image Segmentation Techniques 1
- Co-authors
- Martin Weygandt (2 shared papers)Kerstin Ritter (2 shared papers)Fabian Eitel (2 shared papers)Bernt Schiele (6 shared papers)Mario Fritz (3 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)Frontiers in Aging Neuroscience (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)
- Partner nations
- Germany
In The Last Decade
Moritz Böhle
8 papers receiving 214 citations
Peers
Comparison fields: 5 of 73
- Health Informatics 34
- Artificial Intelligence 133
- Neurology 29
- Health Information Management 14
- Cognitive Neuroscience 34
Countries citing papers authored by Moritz Böhle
This map shows the geographic impact of Moritz Böhle'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 Moritz Böhle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Moritz Böhle more than expected).
Fields of papers citing papers by Moritz Böhle
This network shows the impact of papers produced by Moritz Böhle. 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 Moritz Böhle. The network helps show where Moritz Böhle may publish in the future.
Co-authors
The 5 scholars most cited alongside Moritz Böhle, 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 | 2022 | 30 | |
| 3 | 2022 | 15 | |
| 4 | 2024 | 5 | |
| 5 | 2022 | 3 | |
| 6 | 2024 | 2 | |
| 7 | Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data | 2019 | 2 |
| 8 | 2023 | 1 |
About Moritz Böhle
Moritz Böhle is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Infectious Diseases, having authored 8 papers that have together received 220 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (5 papers), Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (2 papers), Adversarial Robustness in Machine Learning (2 papers), AI in cancer detection (2 papers), Medical Image Segmentation Techniques (1 paper) and Advanced Neuroimaging Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (34 citations), Artificial Intelligence (133 citations), Neurology (29 citations), Health Information Management (14 citations) and Cognitive Neuroscience (34 citations). Moritz Böhle has collaborated with scholars based in Germany. Frequent co-authors include Martin Weygandt, Kerstin Ritter, Fabian Eitel, Bernt Schiele and Mario Fritz. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Frontiers in Aging Neuroscience and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.