A. Mosquera

736 total citations
34 papers, 466 citations indexed

About

A. Mosquera is a scholar working on Public Health, Environmental and Occupational Health, Ophthalmology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, A. Mosquera has authored 34 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Public Health, Environmental and Occupational Health, 12 papers in Ophthalmology and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in A. Mosquera's work include Ocular Surface and Contact Lens (15 papers), Glaucoma and retinal disorders (11 papers) and Medical Image Segmentation Techniques (6 papers). A. Mosquera is often cited by papers focused on Ocular Surface and Contact Lens (15 papers), Glaucoma and retinal disorders (11 papers) and Medical Image Segmentation Techniques (6 papers). A. Mosquera collaborates with scholars based in Spain, United Kingdom and France. A. Mosquera's co-authors include Manuel G. Penedo, Marı́a J. Carreira, D. Cabello, Beatriz Remeseiro, N. Barreira, Carlos García‐Resúa, Xosé M. Pardo, Eva Yebra‐Pimentel, Lucía Ramos and Noelia Sánchez‐Maroño and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Medical Physics and Journal of Hypertension.

In The Last Decade

A. Mosquera

32 papers receiving 440 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
A. Mosquera Spain	12	215	146	129	111	109	34	466
Jianyang Xie China	11	379 1.8×	279 1.9×	68 0.5×	130 1.2×	44 0.4×	31	497
Luana Batista da Cruz Brazil	7	172 0.8×	24 0.2×	21 0.2×	104 0.9×	97 0.9×	20	319
N. Barreira Spain	13	359 1.7×	332 2.3×	103 0.8×	135 1.2×	43 0.4×	49	521
Tahir Mahmood South Korea	13	342 1.6×	88 0.6×	7 0.1×	192 1.7×	232 2.1×	30	620
Swamidoss Issac Niwas India	14	279 1.3×	202 1.4×	26 0.2×	212 1.9×	140 1.3×	19	499
Yanda Meng United Kingdom	11	227 1.1×	71 0.5×	26 0.2×	230 2.1×	252 2.3×	28	504
Yuchen Xie China	12	497 2.3×	358 2.5×	15 0.1×	109 1.0×	79 0.7×	26	748
Siamak Yousefi United States	22	1.7k 8.1×	1.7k 11.5×	152 1.2×	205 1.8×	75 0.7×	85	2.1k

Countries citing papers authored by A. Mosquera

Since Specialization

Citations

This map shows the geographic impact of A. Mosquera'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 A. Mosquera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Mosquera more than expected).

Fields of papers citing papers by A. Mosquera

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Mosquera. 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 A. Mosquera. The network helps show where A. Mosquera may publish in the future.

Co-authorship network of co-authors of A. Mosquera

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

All Works

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20 of 20 papers shown

Brea, Luisa Sánchez, N. Barreira, Noelia Sánchez‐Maroño, et al.. (2016). On the analysis of feature selection techniques in a conjunctival hyperemia grading framework.. The European Symposium on Artificial Neural Networks. 2 indexed citations

Brea, Luisa Sánchez, N. Barreira, A. Mosquera, Katharine Evans, & Hugo Pena‐Verdeal. (2016). Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva. Computational and Mathematical Methods in Medicine. 2016. 1–9. 13 indexed citations

Brea, Luisa Sánchez, N. Barreira, Noelia Sánchez‐Maroño, et al.. (2016). On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings. Artificial Intelligence in Medicine. 71. 30–42. 12 indexed citations

Brea, Luisa Sánchez, N. Barreira, A. Mosquera, Hugo Pena‐Verdeal, & Eva Yebra‐Pimentel. (2016). Comparing Machine Learning Techniques in a Hyperemia Grading Framework. 2 indexed citations

Remeseiro, Beatriz, A. Mosquera, & Manuel G. Penedo. (2015). CASDES: A Computer-Aided System to Support Dry Eye Diagnosis Based on Tear Film Maps. IEEE Journal of Biomedical and Health Informatics. 20(3). 936–943. 16 indexed citations

Remeseiro, Beatriz, A. Mosquera, Manuel G. Penedo, & Carlos García‐Resúa. (2014). Tear Film Maps based on the Lipid Interference Patterns. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 732–739. 5 indexed citations

Remeseiro, Beatriz, Katherine Oliver, Alan Tomlinson, et al.. (2014). Automatic grading system for human tear films. Pattern Analysis and Applications. 18(3). 677–694. 9 indexed citations

Remeseiro, Beatriz, et al.. (2013). Automatic classification of the interferential tear film lipid layer using colour texture analysis. Computer Methods and Programs in Biomedicine. 111(1). 93–103. 18 indexed citations

Ramos, Lucía, N. Barreira, A. Mosquera, et al.. (2013). Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards. Computer Methods and Programs in Biomedicine. 113(3). 715–724. 19 indexed citations

10.

Ramos, Lucía, N. Barreira, A. Mosquera, et al.. (2012). Adaptive parameter computation for the automatic measure of the Tear Break-Up Time.. 243. 1370–1379. 3 indexed citations

11.

Remeseiro, Beatriz, et al.. (2012). Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification. Computational and Mathematical Methods in Medicine. 2012. 1–10. 20 indexed citations

12.

Ramos, Lucía, et al.. (2011). Automation of the tear film break-up time test. 1–5. 10 indexed citations

13.

Mosquera, A.. (2009). Aprendizaje cooperativo en bases de datos. 1 indexed citations

14.

Mosquera, A., et al.. (2006). Object-Based Image Retrieval Using Active Nets. Florence Research (University of Florence). 750–753. 2 indexed citations

15.

Pose‐Reino, Antonio, Francisco Gómez‐Ulla, Marı́a J. Carreira, et al.. (2005). Computerized measurement of retinal blood vessel calibre: description, validation and use to determine the influence of ageing and hypertension. Journal of Hypertension. 23(4). 843–850. 32 indexed citations

16.

Carreira, Marı́a J., D. Cabello, & A. Mosquera. (1999). Automatic Segmentation of Lung Fields on Chest Radiographic Images. Computers and Biomedical Research. 32(3). 283–303. 5 indexed citations

17.

Carreira, Marı́a J., D. Cabello, Manuel G. Penedo, & A. Mosquera. (1998). Computer‐aided diagnoses: Automatic detection of lung nodules. Medical Physics. 25(10). 1998–2006. 35 indexed citations

18.

Penedo, Manuel G., Marı́a J. Carreira, A. Mosquera, & D. Cabello. (1998). Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Transactions on Medical Imaging. 17(6). 872–880. 148 indexed citations

19.

Mosquera, A. & D. Cabello. (1996). The Markov random fields in functional neighbors as a texture model: applications in texture classification. 815–819 vol.2. 5 indexed citations

20.

Mosquera, A.. (1995). Campo aleatorio de Markov con vecindad funcional: modelo generalizado de texturas. Dialnet (Universidad de la Rioja). 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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