D. Schött
Impact in
- Health Informatics top 10%
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- MRI in cancer diagnosis
- Radiation Dose and Imaging
Papers in ⓘ
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- Radiomics and Machine Learning in Medical Imaging 10
- Medical Imaging Techniques and Applications 4
- MRI in cancer diagnosis 4
- Radiation Dose and Imaging 4
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- Advanced Radiotherapy Techniques 3
- Co-authors
- X. Allen Li (8 shared papers)William A. Hall (8 shared papers)Haidy Nasief (2 shared papers)Cheng Zheng (2 shared papers)Susan Tsai (1 shared paper)Beth Erickson (2 shared papers)Bradley A. Erickson (6 shared papers)Paul Knechtges (5 shared papers)
- Journals
- Medical Physics (4 papers)International Journal of Radiation Oncology*Biology*Physics (3 papers)Radiotherapy and Oncology (1 paper)Frontiers in Oncology (1 paper)Translational Oncology (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
D. Schött
15 papers receiving 355 citations
Peers
Comparison fields: 5 of 42
- Health Informatics 19
- Radiology, Nuclear Medicine and Imaging 302
- Oncology 164
- Radiation 31
- Artificial Intelligence 89
Countries citing papers authored by D. Schött
This map shows the geographic impact of D. Schött'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 D. Schött with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Schött more than expected).
Fields of papers citing papers by D. Schött
This network shows the impact of papers produced by D. Schött. 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 D. Schött. The network helps show where D. Schött may publish in the future.
Co-authors
The 25 scholars most cited alongside D. Schött, 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 | 2019 | 127 | |
| 2 | 2020 | 69 | |
| 3 | 2017 | 68 | |
| 4 | 2018 | 35 | |
| 5 | 2020 | 14 | |
| 6 | 2018 | 12 | |
| 7 | 2000 | 11 | |
| 8 | 2021 | 9 | |
| 9 | 2019 | 7 | |
| 10 | 2016 | 2 | |
| 11 | 2019 | 2 | |
| 12 | 2019 | 1 | |
| 13 | 2019 | 1 | |
| 14 | 2016 | 1 | |
| 15 | 2019 | 1 | |
| 16 | 2020 | 0 |
About D. Schött
D. Schött is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation, Biomedical Engineering, Oncology and Obstetrics and Gynecology, having authored 16 papers that have together received 360 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced X-ray and CT Imaging (9 papers), Medical Imaging Techniques and Applications (4 papers), Pancreatic and Hepatic Oncology Research (4 papers), MRI in cancer diagnosis (4 papers), Radiation Dose and Imaging (4 papers), Advanced Radiotherapy Techniques (3 papers) and Welding Techniques and Residual Stresses (2 papers). The work is most often cited by research in Health Informatics (19 citations), Radiology, Nuclear Medicine and Imaging (302 citations), Oncology (164 citations), Radiation (31 citations) and Artificial Intelligence (89 citations). D. Schött has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include X. Allen Li, William A. Hall, Haidy Nasief, Cheng Zheng, Susan Tsai, Beth Erickson, Bradley A. Erickson, Paul Knechtges, E.S. Paulson and Kiyoko Oshima. Their work appears in journals such as Medical Physics, International Journal of Radiation Oncology*Biology*Physics, Radiotherapy and Oncology, Frontiers in Oncology and Translational Oncology.
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.