Patrick Schelb
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Prostate Cancer Diagnosis and Treatment
- Prostate Cancer Treatment and Research
Papers in
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- Prostate Cancer Diagnosis and Treatment 6
- Prostate Cancer Treatment and Research 3
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- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- David Bonekamp (6 shared papers)Markus Hohenfellner (6 shared papers)Jan Philipp Radtke (5 shared papers)Manuel Wiesenfarth (5 shared papers)Tristan Anselm Kuder (5 shared papers)Klaus Maier‐Hein (4 shared papers)Philipp Kickingereder (3 shared papers)Simon Köhl (3 shared papers)
- Journals
- European Radiology (2 papers)Radiology (2 papers)RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (1 paper)Magnetic Resonance Imaging (1 paper)
- Partner nations
- GermanyUnited StatesChina
In The Last Decade
Patrick Schelb
6 papers receiving 504 citations
Patrick Schelb's Hit Papers
Peers
Comparison fields: 5 of 37
- Health Informatics 37
- Pulmonary and Respiratory Medicine 349
- Radiology, Nuclear Medicine and Imaging 211
- Artificial Intelligence 89
- Computer Vision and Pattern Recognition 33
Countries citing papers authored by Patrick Schelb
This map shows the geographic impact of Patrick Schelb'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 Patrick Schelb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Schelb more than expected).
Fields of papers citing papers by Patrick Schelb
This network shows the impact of papers produced by Patrick Schelb. 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 Patrick Schelb. The network helps show where Patrick Schelb may publish in the future.
Co-authors
The 24 scholars most cited alongside Patrick Schelb, 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 | Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment Hit paper breakdown → | 2019 | 249 |
| 2 | 2018 | 159 | |
| 3 | 2020 | 36 | |
| 4 | 2018 | 26 | |
| 5 | 2020 | 20 | |
| 6 | 2021 | 19 |
About Patrick Schelb
Patrick Schelb is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Infectious Diseases, Organic Chemistry and Surgery, having authored 6 papers that have together received 509 indexed citations. Recurring topics across this work include Prostate Cancer Diagnosis and Treatment (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Prostate Cancer Treatment and Research (3 papers). The work is most often cited by research in Health Informatics (37 citations), Pulmonary and Respiratory Medicine (349 citations), Radiology, Nuclear Medicine and Imaging (211 citations), Artificial Intelligence (89 citations) and Computer Vision and Pattern Recognition (33 citations). Patrick Schelb has collaborated with scholars based in Germany, United States and China. Frequent co-authors include David Bonekamp, Markus Hohenfellner, Jan Philipp Radtke, Manuel Wiesenfarth, Tristan Anselm Kuder, Klaus Maier‐Hein, Philipp Kickingereder, Simon Köhl, Albrecht Stenzinger and Heinz-Peter Schlemmer. Their work appears in journals such as European Radiology, Radiology, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren and Magnetic Resonance Imaging.
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.