Kersten Petersen
Impact in
- Health Informatics top 5%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Medical Image Segmentation Techniques 4
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- Cardiac Imaging and Diagnostics 3
- Radiomics and Machine Learning in Medical Imaging 2
- Co-authors
- Mads NielsenChristian IgelFrançois LauzeErik B. DamMartin LillholmRikke Rass WinkelCeline M. VachonKatharina Holland
- Journals
- IEEE Transactions on Medical Imaging (4 papers)Phytotherapy Research (1 paper)European Heart Journal - Cardiovascular Imaging (1 paper)BMC Cancer (1 paper)Machine Vision and Applications (1 paper)
- Partner nations
- DenmarkUnited StatesUnited Kingdom
In The Last Decade
Kersten Petersen
13 papers receiving 917 citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Health Informatics 30
- Radiology, Nuclear Medicine and Imaging 449
- Computer Vision and Pattern Recognition 257
- Artificial Intelligence 404
- Neurology 79
Countries citing papers authored by Kersten Petersen
This map shows the geographic impact of Kersten Petersen'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 Kersten Petersen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kersten Petersen more than expected).
Fields of papers citing papers by Kersten Petersen
This network shows the impact of papers produced by Kersten Petersen. 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 Kersten Petersen. The network helps show where Kersten Petersen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kersten Petersen, 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 | 2024 | 1 | |
| 2 | 2024 | 23 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 29 | |
| 5 | 2022 | 18 | |
| 6 | 2019 | 75 | |
| 7 | Template Transformer Networks for Image Segmentation | 2019 | 3 |
| 8 | 2016 | 35 | |
| 9 | Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring Hit paper breakdown → | 2016 | 319 |
| 10 | Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network Hit paper breakdown → | 2013 | 394 |
| 11 | 2012 | 7 | |
| 12 | 2011 | 7 | |
| 13 | 2008 | 38 |
About Kersten Petersen
Kersten Petersen is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Oral Surgery, Biomedical Engineering and Rheumatology, having authored 13 papers that have together received 951 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (4 papers), Advanced X-ray and CT Imaging (4 papers), Coronary Interventions and Diagnostics (3 papers), Cardiac Imaging and Diagnostics (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), AI in cancer detection (2 papers), Medical Imaging and Analysis (2 papers) and Osteoarthritis Treatment and Mechanisms (2 papers). The work is most often cited by research in Health Informatics (30 citations), Radiology, Nuclear Medicine and Imaging (449 citations), Computer Vision and Pattern Recognition (257 citations), Artificial Intelligence (404 citations) and Neurology (79 citations). Kersten Petersen has collaborated with scholars based in Denmark, United States and United Kingdom. Frequent co-authors include Mads Nielsen, Christian Igel, François Lauze, Erik B. Dam, Martin Lillholm, Rikke Rass Winkel, Celine M. Vachon, Katharina Holland, Michiel Kallenberg and Andrew Y. Ng. Their work appears in journals such as IEEE Transactions on Medical Imaging, Phytotherapy Research, European Heart Journal - Cardiovascular Imaging, BMC Cancer and Machine Vision and Applications.
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.