Serkan Çimen
Impact in
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- Cardiac Imaging and Diagnostics
- Medical Imaging Techniques and Applications
- Advanced MRI Techniques and Applications
Papers in
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- Cardiac Imaging and Diagnostics 4
- Medical Imaging Techniques and Applications 3
- Radiomics and Machine Learning in Medical Imaging 1
- COVID-19 diagnosis using AI 1
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- Medical Image Segmentation Techniques 3
- Co-authors
- Alejandro F. Frangi (4 shared papers)Ali Gooya (2 shared papers)Michael Graß (1 shared paper)Nishant Ravikumar (1 shared paper)Zeike A. Taylor (1 shared paper)Nenad Filipović (1 shared paper)Arso M. Vukićević (1 shared paper)Nikola Jagić (1 shared paper)
- Journals
- Medical Image Analysis (2 papers)European Journal of Radiology (1 paper)Applied Sciences (1 paper)Scientific Reports (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Serkan Çimen
7 papers receiving 192 citations
Peers
Comparison fields: 5 of 43
- Health Informatics 7
- Radiology, Nuclear Medicine and Imaging 112
- Computer Vision and Pattern Recognition 52
- Computer Graphics and Computer-Aided Design 8
- Computational Mathematics 1
Countries citing papers authored by Serkan Çimen
This map shows the geographic impact of Serkan Çimen'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 Serkan Çimen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Serkan Çimen more than expected).
Fields of papers citing papers by Serkan Çimen
This network shows the impact of papers produced by Serkan Çimen. 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 Serkan Çimen. The network helps show where Serkan Çimen may publish in the future.
Co-authors
The 25 scholars most cited alongside Serkan Çimen, 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 | 2016 | 80 | |
| 2 | 2020 | 52 | |
| 3 | 2017 | 29 | |
| 4 | 2018 | 27 | |
| 5 | 2014 | 3 | |
| 6 | 2022 | 1 | |
| 7 | Training Deep Networks on Domain Randomized Synthetic X-ray Data for Cardiac Interventions | 2018 | 1 |
About Serkan Çimen
Serkan Çimen is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Biomedical Engineering, Surgery and Geometry and Topology, having authored 7 papers that have together received 193 indexed citations. Recurring topics across this work include Cardiac Imaging and Diagnostics (4 papers), Medical Image Segmentation Techniques (3 papers), Medical Imaging Techniques and Applications (3 papers), Advanced X-ray and CT Imaging (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), COVID-19 diagnosis using AI (1 paper), Morphological variations and asymmetry (1 paper) and Medical Imaging and Analysis (1 paper). The work is most often cited by research in Health Informatics (7 citations), Radiology, Nuclear Medicine and Imaging (112 citations), Computer Vision and Pattern Recognition (52 citations), Computer Graphics and Computer-Aided Design (8 citations) and Computational Mathematics (1 citation). Serkan Çimen has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Alejandro F. Frangi, Ali Gooya, Michael Graß, Nishant Ravikumar, Zeike A. Taylor, Nenad Filipović, Arso M. Vukićević, Nikola Jagić, Gordana Jovičić and Jeremy R. Burt. Their work appears in journals such as Medical Image Analysis, European Journal of Radiology, Applied Sciences, Scientific Reports and Lecture notes in computer science.
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.