Aurora Sáez
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Cutaneous Melanoma Detection and Management
Papers in
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- Medical Image Segmentation Techniques 2
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- Color Science and Applications 4
- Co-authors
- Begoña Acha (12 shared papers)Carmen Serrano (11 shared papers)Pedro Antonio Gutiérrez (3 shared papers)César Hervás‐Martínez (3 shared papers)Javier Sánchez‐Monedero (3 shared papers)Luis M. Escudero (5 shared papers)Alberto Pascual (3 shared papers)Yifeng Zhao (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (2 papers)Scientific Reports (1 paper)Journal of Biomedical Optics (1 paper)BMC Medicine (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)
- Partner nations
- SpainNetherlands
In The Last Decade
Aurora Sáez
17 papers receiving 285 citations
Peers
Comparison fields: 5 of 63
- Biophysics 33
- Oncology 122
- Artificial Intelligence 105
- Computer Vision and Pattern Recognition 41
- Cell Biology 32
Countries citing papers authored by Aurora Sáez
This map shows the geographic impact of Aurora Sáez'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 Aurora Sáez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurora Sáez more than expected).
Fields of papers citing papers by Aurora Sáez
This network shows the impact of papers produced by Aurora Sáez. 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 Aurora Sáez. The network helps show where Aurora Sáez may publish in the future.
Co-authors
The 25 scholars most cited alongside Aurora Sáez, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 62 | |
| 2 | 2020 | 60 | |
| 3 | 2015 | 54 | |
| 4 | 2013 | 18 | |
| 5 | 2013 | 16 | |
| 6 | 2017 | 16 | |
| 7 | 2013 | 12 | |
| 8 | 2016 | 12 | |
| 9 | 2018 | 11 | |
| 10 | 2013 | 11 | |
| 11 | 2017 | 7 | |
| 12 | 2014 | 4 | |
| 13 | 2013 | 2 | |
| 14 | 2010 | 2 | |
| 15 | 2012 | 2 | |
| 16 | 2013 | 1 | |
| 17 | 2010 | 1 | |
| 18 | 2012 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2024 | 0 |
About Aurora Sáez
Aurora Sáez is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics, Oncology, Molecular Biology and Cell Biology, having authored 21 papers that have together received 293 indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (5 papers), Industrial Vision Systems and Defect Detection (4 papers), Color Science and Applications (4 papers), AI in cancer detection (3 papers), Glaucoma and retinal disorders (2 papers), Genetic Neurodegenerative Diseases (2 papers), Medical Image Segmentation Techniques (2 papers) and Cellular Mechanics and Interactions (2 papers). The work is most often cited by research in Biophysics (33 citations), Oncology (122 citations), Artificial Intelligence (105 citations), Computer Vision and Pattern Recognition (41 citations) and Cell Biology (32 citations). Aurora Sáez has collaborated with scholars based in Spain and Netherlands. Frequent co-authors include Begoña Acha, Carmen Serrano, Pedro Antonio Gutiérrez, César Hervás‐Martínez, Javier Sánchez‐Monedero, Luis M. Escudero, Alberto Pascual, Yifeng Zhao, Can Han and Guangtao Yang. Their work appears in journals such as IEEE Transactions on Medical Imaging, Scientific Reports, Journal of Biomedical Optics, BMC Medicine and IEEE Journal of Biomedical and Health Informatics.
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.