Roland Opfer
- Radiology, Nuclear Medicine and Imaging top 5%
- Pathology and Forensic Medicine top 10%
- Mechanics of Materials top 10%
- Pulmonary and Respiratory Medicine
- Artificial Intelligence top 10%
- Co-authors
- Sven SchipplingRobert SchabackLeevan LingRafael WiemkerLothar SpiesAnn‐Christin OstwaldtPraveena ManogaranPer Suppa
- Topics
- Multiple Sclerosis Research Studies (17 papers)Radiomics and Machine Learning in Medical Imaging (15 papers)Medical Imaging Techniques and Applications (11 papers)
- Journals
- SHILAP Revista de lepidopterologíaNeurologyScientific Reports
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Roland Opfer
51 papers receiving 719 citations
Peers
Comparison fields: 5 of 102
- Radiology, Nuclear Medicine and Imaging 258
- Pathology and Forensic Medicine 200
- Mechanics of Materials 128
- Pulmonary and Respiratory Medicine 99
- Artificial Intelligence 98
Countries citing papers authored by Roland Opfer
This map shows the geographic impact of Roland Opfer'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 Roland Opfer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roland Opfer more than expected).
Fields of papers citing papers by Roland Opfer
This network shows the impact of papers produced by Roland Opfer. 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 Roland Opfer. The network helps show where Roland Opfer may publish in the future.
Co-authorship network of co-authors of Roland Opfer
This figure shows the co-authorship network connecting the top 25 collaborators of Roland Opfer. A scholar is included among the top collaborators of Roland Opfer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Roland Opfer. Roland Opfer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 0 | |
| 10 | 7 | |
| 11 | 8 | |
| 12 | 19 | |
| 13 | 13 | |
| 14 | 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation | 1 |
| 15 | 43 | |
| 16 | 18 | |
| 17 | 23 | |
| 18 | 84 | |
| 19 | 9 | |
| 20 | 18 |
About Roland Opfer
Roland Opfer is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Health Informatics, having authored 54 papers that have together received 759 indexed citations. Recurring topics across this work include Multiple Sclerosis Research Studies (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Medical Imaging Techniques and Applications (11 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (258 citations), Pathology and Forensic Medicine (200 citations) and Neurology (76 citations). Roland Opfer has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Sven Schippling, Robert Schaback, Leevan Ling, Rafael Wiemker, Lothar Spies, Ann‐Christin Ostwaldt, Praveena Manogaran, Per Suppa, Maria Pia Sormani and Alexander Schlaefer. Their work appears in journals such as SHILAP Revista de lepidopterología, Neurology and Scientific Reports.
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.