Urspeter Knecht
Impact in
Papers in
- Genetics 13
- Glioma Diagnosis and Treatment 13
-
- Radiomics and Machine Learning in Medical Imaging 8
- Advanced MRI Techniques and Applications 4
- Medical Imaging Techniques and Applications 3
- MRI in cancer diagnosis 2
- Co-authors
- Roland Wiest (14 shared papers)Mauricio Reyes (11 shared papers)Raphael Meier (10 shared papers)Johannes Slotboom (9 shared papers)Philippe Schucht (7 shared papers)Stefan Bauer (1 shared paper)Ekkehard Hewer (4 shared papers)Yannick Suter (3 shared papers)
- Journals
- Neuro-Oncology (2 papers)NMR in Biomedicine (2 papers)Scientific Reports (1 paper)Clinical Neurology and Neurosurgery (1 paper)Diagnostic Cytopathology (1 paper)
- Partner nations
- SwitzerlandAustriaPortugal
In The Last Decade
Urspeter Knecht
19 papers receiving 339 citations
Peers
Comparison fields: 5 of 51
- Genetics 120
- Neurology 67
- Radiology, Nuclear Medicine and Imaging 149
- Health Informatics 8
- Computer Vision and Pattern Recognition 46
Countries citing papers authored by Urspeter Knecht
This map shows the geographic impact of Urspeter Knecht'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 Urspeter Knecht with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Urspeter Knecht more than expected).
Fields of papers citing papers by Urspeter Knecht
This network shows the impact of papers produced by Urspeter Knecht. 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 Urspeter Knecht. The network helps show where Urspeter Knecht may publish in the future.
Co-authors
The 25 scholars most cited alongside Urspeter Knecht, 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 | 2020 | 45 | |
| 4 | 2017 | 30 | |
| 5 | 2016 | 26 | |
| 6 | 2016 | 22 | |
| 7 | 2022 | 16 | |
| 8 | 2019 | 15 | |
| 9 | 2020 | 13 | |
| 10 | 2007 | 9 | |
| 11 | 2016 | 7 | |
| 12 | 2019 | 5 | |
| 13 | 2018 | 5 | |
| 14 | A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases | 2016 | 4 |
| 15 | 2018 | 3 | |
| 16 | 2018 | 3 | |
| 17 | 2023 | 2 | |
| 18 | 2018 | 2 | |
| 19 | 2014 | 2 | |
| 20 | 2020 | 0 |
About Urspeter Knecht
Urspeter Knecht is a scholar working on Genetics, Radiology, Nuclear Medicine and Imaging, Neurology, Epidemiology and Artificial Intelligence, having authored 20 papers that have together received 341 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (13 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Brain Tumor Detection and Classification (5 papers), Advanced MRI Techniques and Applications (4 papers), Medical Imaging Techniques and Applications (3 papers), MRI in cancer diagnosis (2 papers), Urologic and reproductive health conditions (1 paper) and Antifungal resistance and susceptibility (1 paper). The work is most often cited by research in Genetics (120 citations), Neurology (67 citations), Radiology, Nuclear Medicine and Imaging (149 citations), Health Informatics (8 citations) and Computer Vision and Pattern Recognition (46 citations). Urspeter Knecht has collaborated with scholars based in Switzerland, Austria and Portugal. Frequent co-authors include Roland Wiest, Mauricio Reyes, Raphael Meier, Johannes Slotboom, Philippe Schucht, Stefan Bauer, Ekkehard Hewer, Yannick Suter, Richard McKinley and Waldo Valenzuela. Their work appears in journals such as Neuro-Oncology, NMR in Biomedicine, Scientific Reports, Clinical Neurology and Neurosurgery and Diagnostic Cytopathology.
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.