Aurora Sáez

477 total citations
21 papers, 293 citations indexed

About

Aurora Sáez is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Oncology. According to data from OpenAlex, Aurora Sáez has authored 21 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Atomic and Molecular Physics, and Optics and 5 papers in Oncology. Recurrent topics in Aurora Sáez's work include Cutaneous Melanoma Detection and Management (5 papers), Color Science and Applications (4 papers) and Industrial Vision Systems and Defect Detection (4 papers). Aurora Sáez is often cited by papers focused on Cutaneous Melanoma Detection and Management (5 papers), Color Science and Applications (4 papers) and Industrial Vision Systems and Defect Detection (4 papers). Aurora Sáez collaborates with scholars based in Spain and Netherlands. Aurora Sáez's co-authors include Begoña Acha, Carmen Serrano, Javier Sánchez‐Monedero, Pedro Antonio Gutiérrez, César Hervás‐Martínez, Luis M. Escudero, Paul Prócel, Luana Mazzarella, Miro Zeman and Can Han and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Image Processing.

In The Last Decade

Aurora Sáez

17 papers receiving 285 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Aurora Sáez Spain 10 122 105 61 41 39 21 293
Chao Xin China 11 116 1.0× 86 0.8× 28 0.5× 26 0.6× 77 2.0× 26 388
Marcin Kociołek Poland 9 56 0.5× 82 0.8× 9 0.1× 62 1.5× 41 1.1× 17 315
Maria Gabrani Switzerland 12 53 0.4× 254 2.4× 38 0.6× 164 4.0× 54 1.4× 39 469
Tomáš Majtner Czechia 10 130 1.1× 122 1.2× 5 0.1× 73 1.8× 51 1.3× 23 341
Michele D’Orazio Italy 12 17 0.1× 21 0.2× 36 0.6× 22 0.5× 70 1.8× 35 332
Hao Dai China 10 50 0.4× 59 0.6× 20 0.3× 14 0.3× 90 2.3× 51 365
Sachin V. Patwardhan United States 8 79 0.6× 60 0.6× 9 0.1× 16 0.4× 17 0.4× 20 445
Symon Cotton United Kingdom 8 187 1.5× 55 0.5× 4 0.1× 30 0.7× 39 1.0× 11 433
Shenghua He United States 8 15 0.1× 45 0.4× 39 0.6× 80 2.0× 88 2.3× 25 355
Jerry A. Thomas United States 16 82 0.7× 260 2.5× 97 1.6× 57 1.4× 20 0.5× 30 583

Countries citing papers authored by Aurora Sáez

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Aurora Sáez

This figure shows the co-authorship network connecting the top 25 collaborators of Aurora Sáez. A scholar is included among the top collaborators of Aurora Sáez 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 Aurora Sáez. Aurora Sáez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sáez, Aurora, et al.. (2024). Design and validation of PACTUS: A gamified electronic device for stroke rehabilitation. Computer Methods and Programs in Biomedicine. 260. 108563–108563.
2.
Sáez, Aurora, et al.. (2023). FRAMEWORK FOR EVALUATION OF PROCEDURES FOR HDR LUMINANCE IMAGING MEASUREMENTS. 1004–1013. 1 indexed citations
3.
Prócel, Paul, Aurora Sáez, Luana Mazzarella, et al.. (2020). The role of heterointerfaces and subgap energy states on transport mechanisms in silicon heterojunction solar cells. Progress in Photovoltaics Research and Applications. 28(9). 935–945. 60 indexed citations
4.
Sáez, Aurora, et al.. (2018). Statistical Detection of Colors in Dermoscopic Images With a Texton-Based Estimation of Probabilities. IEEE Journal of Biomedical and Health Informatics. 23(2). 560–569. 11 indexed citations
5.
Sáez, Aurora, et al.. (2017). Rules of tissue packing involving different cell types: human muscle organization. Scientific Reports. 7(1). 40444–40444. 7 indexed citations
6.
Sánchez‐Monedero, Javier, María Pérez‐Ortiz, Aurora Sáez, Pedro Antonio Gutiérrez, & César Hervás‐Martínez. (2017). Partial order label decomposition approaches for melanoma diagnosis. Applied Soft Computing. 64. 341–355. 16 indexed citations
7.
Rivas, Eloy, Aurora Sáez, Carmen Paradas, et al.. (2015). Quantifiable diagnosis of neuromuscular diseases through network analysis. Neuromuscular Disorders. 25. S243–S243.
8.
Sáez, Aurora, Javier Sánchez‐Monedero, Pedro Antonio Gutiérrez, & César Hervás‐Martínez. (2015). Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images. IEEE Transactions on Medical Imaging. 35(4). 1036–1045. 54 indexed citations
9.
Sáez, Aurora, Carmen Serrano, & Begoña Acha. (2014). Normalized Cut optimization based on color perception findings. A comparative study. Machine Vision and Applications. 25(7). 1813–1823. 4 indexed citations
10.
Sáez, Aurora, Carmen Serrano, & Begoña Acha. (2014). Model-Based Classification Methods of Global Patterns in Dermoscopic Images. IEEE Transactions on Medical Imaging. 33(5). 1137–1147. 62 indexed citations
11.
Fondón, Irene, Mark J. J. P. van Grinsven, Clara I. Sá‎nchez, & Aurora Sáez. (2013). Perceptually adapted method for optic disc detection on retinal fundus images. 2. 279–284. 2 indexed citations
12.
Sáez, Aurora, et al.. (2013). Topological Progression in Proliferating Epithelia Is Driven by a Unique Variation in Polygon Distribution. PLoS ONE. 8(11). e79227–e79227. 16 indexed citations
13.
Sáez, Aurora, Eloy Rivas, Carmen Paradas, et al.. (2013). Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis. BMC Medicine. 11(1). 77–77. 18 indexed citations
14.
Sáez, Aurora, et al.. (2013). Neuromuscular disease classification system. Journal of Biomedical Optics. 18(6). 66017–66017. 12 indexed citations
15.
Mendoza, Carlos S., J. A. Pérez‐Carrasco, Aurora Sáez, Begoña Acha, & Carmen Serrano. (2013). Linearized Multidimensional Earth-Mover's-Distance Gradient Flows. IEEE Transactions on Image Processing. 22(12). 5322–5335. 1 indexed citations
16.
Sáez, Aurora, Carlos S. Mendoza, Begoña Acha, & Carmen Serrano. (2013). Development and evaluation of perceptually adapted colour gradients. IET Image Processing. 7(4). 355–363. 11 indexed citations
17.
Sáez, Aurora, Irene Fondón, Begoña Acha, et al.. (2012). Optic Disc segmentation based on level-set and colour gradients. Conference on Colour in Graphics Imaging and Vision. 6(1). 121–125. 2 indexed citations
18.
Sáez, Aurora, Carmen Serrano, & Begoña Acha. (2012). A Review on CAD Tools for Burn Diagnosis. 181–202. 1 indexed citations
19.
Sáez, Aurora, Carmen Serrano, & Begoña Acha. (2010). Evaluation Perceptual Color Edge Detection Algorithms. Conference on Colour in Graphics Imaging and Vision. 5(1). 222–227. 2 indexed citations
20.
Sáez, Aurora, Begoña Acha, & Carmen Serrano. (2010). Segmentation and classification of dermatological lesions. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7624. 76243L–76243L. 1 indexed citations

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.

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