Timo M. Deist
- Radiology, Nuclear Medicine and Imaging top 0.2%
- Pulmonary and Respiratory Medicine top 1%
- Biomedical Engineering top 5%
- Oncology top 5%
- Artificial Intelligence top 2%
- Co-authors
- André DekkerArthur JochemsPhilippe LambinJohan van SoestSeán WalshYvonka van WijkRalph T. H. LeijenaarJanita E. van Timmeren
- Topics
- Radiomics and Machine Learning in Medical Imaging (6 papers)Lung Cancer Diagnosis and Treatment (3 papers)Advanced X-ray and CT Imaging (3 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Journals
- Journal of Clinical OncologyBioinformaticsInternational Journal of Radiation Oncology*Biology*Physics
- Partner nations
- NetherlandsUnited StatesGermany
In The Last Decade
Timo M. Deist
15 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Radiology, Nuclear Medicine and Imaging 3.6k
- Pulmonary and Respiratory Medicine 1.5k
- Biomedical Engineering 927
- Oncology 791
- Artificial Intelligence 684
Countries citing papers authored by Timo M. Deist
This map shows the geographic impact of Timo M. Deist'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 Timo M. Deist with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo M. Deist more than expected).
Fields of papers citing papers by Timo M. Deist
This network shows the impact of papers produced by Timo M. Deist. 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 Timo M. Deist. The network helps show where Timo M. Deist may publish in the future.
Co-authorship network of co-authors of Timo M. Deist
This figure shows the co-authorship network connecting the top 25 collaborators of Timo M. Deist. A scholar is included among the top collaborators of Timo M. Deist 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 Timo M. Deist. Timo M. Deist is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 42 | |
| 2 | 41 | |
| 3 | 23 | |
| 4 | 22 | |
| 5 | 2 | |
| 6 | 87 | |
| 7 | 90 | |
| 8 | Radiomics: the bridge between medical imaging and personalized medicinebreakdown → | 3729 |
| 9 | 23 | |
| 10 | 3 | |
| 11 | 112 | |
| 12 | 1 | |
| 13 | 0 | |
| 14 | 198 | |
| 15 | 53 | |
| 16 | 8 |
About Timo M. Deist
Timo M. Deist is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Pulmonary and Respiratory Medicine, having authored 16 papers that have together received 4.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), Lung Cancer Diagnosis and Treatment (3 papers) and Advanced X-ray and CT Imaging (3 papers). The work is most often cited by research in Health Informatics (230 citations), Radiology, Nuclear Medicine and Imaging (3.6k citations) and Pulmonary and Respiratory Medicine (1.5k citations). Timo M. Deist has collaborated with scholars based in Netherlands, United States and Germany. Frequent co-authors include André Dekker, Arthur Jochems, Philippe Lambin, Johan van Soest, Seán Walsh, Yvonka van Wijk, Ralph T. H. Leijenaar, Janita E. van Timmeren, Wouter van Elmpt and Evelyn E.C. de Jong. Their work appears in journals such as Journal of Clinical Oncology, Bioinformatics and International Journal of Radiation Oncology*Biology*Physics.
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.