Diana Mateus
- Computer Vision and Pattern Recognition top 1%
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering top 10%
- Surgery
- Co-authors
- Nassir NavabLoren SchwarzAnne L. MartelDanail StoyanovPurang AbolmaesumiDaniel RacoceanuMaría A. ZuluagaGustavo Carneiro
- Topics
- Radiomics and Machine Learning in Medical Imaging (23 papers)AI in cancer detection (15 papers)Medical Imaging Techniques and Applications (8 papers)
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Medical ImagingPattern Recognition
- Partner nations
- FranceGermanyUnited Kingdom
In The Last Decade
Diana Mateus
59 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 123
- Computer Vision and Pattern Recognition 683
- Radiology, Nuclear Medicine and Imaging 492
- Artificial Intelligence 431
- Biomedical Engineering 259
- Surgery 146
Countries citing papers authored by Diana Mateus
This map shows the geographic impact of Diana Mateus'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 Diana Mateus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diana Mateus more than expected).
Fields of papers citing papers by Diana Mateus
This network shows the impact of papers produced by Diana Mateus. 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 Diana Mateus. The network helps show where Diana Mateus may publish in the future.
Co-authorship network of co-authors of Diana Mateus
This figure shows the co-authorship network connecting the top 25 collaborators of Diana Mateus. A scholar is included among the top collaborators of Diana Mateus 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 Diana Mateus. Diana Mateus 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 | 3 | |
| 4 | 9 | |
| 5 | 72 | |
| 6 | 19 | |
| 7 | 7 | |
| 8 | 127 | |
| 9 | 44 | |
| 10 | 34 | |
| 11 | 25 | |
| 12 | 34 | |
| 13 | 15 | |
| 14 | 8 | |
| 15 | 4 | |
| 16 | 1 | |
| 17 | 1 | |
| 18 | 6 | |
| 19 | ImageCLEF 2010 Working Notes on the Modality Classification Subtask. | 1 |
| 20 | 16 |
About Diana Mateus
Diana Mateus is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 63 papers that have together received 1.5k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (23 papers), AI in cancer detection (15 papers) and Medical Imaging Techniques and Applications (8 papers). The work is most often cited by research in Health Informatics (62 citations), Computer Vision and Pattern Recognition (683 citations) and Radiology, Nuclear Medicine and Imaging (492 citations). Diana Mateus has collaborated with scholars based in France, Germany and United Kingdom. Frequent co-authors include Nassir Navab, Loren Schwarz, Anne L. Martel, Danail Stoyanov, Purang Abolmaesumi, Daniel Racoceanu, María A. Zuluaga, Gustavo Carneiro, Marco Loog and Andrew P. Bradley. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging and Pattern Recognition.
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.