Peter M. Full
Impact in
- Health Informatics top 1%
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- Radiomics and Machine Learning in Medical Imaging
- Cardiac Imaging and Diagnostics
- Advanced MRI Techniques and Applications
Papers in
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- Cardiac Imaging and Diagnostics 3
- Radiomics and Machine Learning in Medical Imaging 3
- COVID-19 diagnosis using AI 2
- Advanced MRI Techniques and Applications 2
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- Advanced Neural Network Applications 3
- Co-authors
- Sandy Engelhardt (3 shared papers)Ivo Wolf (3 shared papers)Klaus Hermann Maier-Hein (6 shared papers)Fabian Isensee (4 shared papers)Paul F. Jaeger (2 shared papers)Jens Kleesiek (2 shared papers)Jan Sellner (1 shared paper)Gregor Koehler (1 shared paper)
In The Last Decade
Peter M. Full
15 papers receiving 2.4k citations
Peter M. Full's Hit Papers
Peers
Comparison fields: 5 of 98
- Health Informatics 134
- Radiology, Nuclear Medicine and Imaging 1.2k
- Computer Vision and Pattern Recognition 1.1k
- Neurology 244
- Artificial Intelligence 616
Countries citing papers authored by Peter M. Full
This map shows the geographic impact of Peter M. Full'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 Peter M. Full with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter M. Full more than expected).
Fields of papers citing papers by Peter M. Full
This network shows the impact of papers produced by Peter M. Full. 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 Peter M. Full. The network helps show where Peter M. Full may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter M. Full, 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 | Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? Hit paper breakdown → | 2018 | 1617 |
| 2 | 2018 | 266 | |
| 3 | 2018 | 193 | |
| 4 | 2021 | 73 | |
| 5 | 2018 | 70 | |
| 6 | 2020 | 68 | |
| 7 | 2022 | 42 | |
| 8 | 2018 | 28 | |
| 9 | 2020 | 18 | |
| 10 | 2023 | 12 | |
| 11 | 2022 | 7 | |
| 12 | 2019 | 5 | |
| 13 | 2021 | 4 | |
| 14 | 2021 | 3 | |
| 15 | 2025 | 1 | |
| 16 | 2025 | 0 |
About Peter M. Full
Peter M. Full is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Health Information Management, Artificial Intelligence and Cardiology and Cardiovascular Medicine, having authored 16 papers that have together received 2.4k indexed citations. Recurring topics across this work include Cardiac Imaging and Diagnostics (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Machine Learning in Healthcare (3 papers), Advanced Neural Network Applications (3 papers), Biomedical Text Mining and Ontologies (2 papers), Artificial Intelligence in Healthcare (2 papers), COVID-19 diagnosis using AI (2 papers) and Advanced MRI Techniques and Applications (2 papers). The work is most often cited by research in Health Informatics (134 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations), Computer Vision and Pattern Recognition (1.1k citations), Neurology (244 citations) and Artificial Intelligence (616 citations). Peter M. Full has collaborated with scholars based in Germany, Canada and China. Frequent co-authors include Sandy Engelhardt, Ivo Wolf, Klaus Hermann Maier-Hein, Fabian Isensee, Paul F. Jaeger, Jens Kleesiek, Jan Sellner, Gregor Koehler, Beat Peter Müller-Stich and Manuel Wiesenfarth. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, The Journal of Clinical Endocrinology & Metabolism, Journal of Cardiovascular Magnetic Resonance and Nature Communications.
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.