Thomas M. Deserno
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Biomedical Engineering top 10%
- Radiology, Nuclear Medicine and Imaging top 5%
- Cardiology and Cardiovascular Medicine top 10%
- Co-authors
- Daniel HaakSameer AntaniMostafa HaghiJoana M. WarneckeL. Rodney LongNagarajan GanapathyJu WangStephan Jonas
- Topics
- Image Retrieval and Classification Techniques (29 papers)AI in cancer detection (25 papers)Non-Invasive Vital Sign Monitoring (23 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- GermanyIndiaUnited States
In The Last Decade
Thomas M. Deserno
169 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 162
- Computer Vision and Pattern Recognition 715
- Artificial Intelligence 502
- Biomedical Engineering 408
- Radiology, Nuclear Medicine and Imaging 335
- Cardiology and Cardiovascular Medicine 218
Countries citing papers authored by Thomas M. Deserno
This map shows the geographic impact of Thomas M. Deserno'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 Thomas M. Deserno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas M. Deserno more than expected).
Fields of papers citing papers by Thomas M. Deserno
This network shows the impact of papers produced by Thomas M. Deserno. 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 Thomas M. Deserno. The network helps show where Thomas M. Deserno may publish in the future.
Co-authorship network of co-authors of Thomas M. Deserno
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas M. Deserno. A scholar is included among the top collaborators of Thomas M. Deserno 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 Thomas M. Deserno. Thomas M. Deserno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 8 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 0 | |
| 10 | 0 | |
| 11 | 4 | |
| 12 | 7 | |
| 13 | 3 | |
| 14 | 70 | |
| 15 | 10 | |
| 16 | IRMA Code II - A New Concept for Classification of Medical Images. | 1 |
| 17 | 56 | |
| 18 | 57 | |
| 19 | Bildverarbeitung fr die Medizin 2008: Algorithmen - Systeme - Anwendungen (Informatik aktuell) | 2 |
| 20 | Baseline Results for the CLEF 2007 Medical Automatic Annotation Task | 1 |
About Thomas M. Deserno
Thomas M. Deserno is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Health Information Management, having authored 187 papers that have together received 2.1k indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (29 papers), AI in cancer detection (25 papers) and Non-Invasive Vital Sign Monitoring (23 papers). The work is most often cited by research in Health Informatics (52 citations), Computer Vision and Pattern Recognition (715 citations) and Health Information Management (76 citations). Thomas M. Deserno has collaborated with scholars based in Germany, India and United States. Frequent co-authors include Daniel Haak, Sameer Antani, Mostafa Haghi, Joana M. Warnecke, L. Rodney Long, Nagarajan Ganapathy, Ju Wang, Stephan Jonas, Nicolai Spicher and Ramakrishnan Swaminathan. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE 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.