José Sigut

1.5k total citations · 1 hit paper
47 papers, 1.0k citations indexed

About

José Sigut is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, José Sigut has authored 47 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Ophthalmology, 15 papers in Radiology, Nuclear Medicine and Imaging and 14 papers in Artificial Intelligence. Recurrent topics in José Sigut's work include Retinal Imaging and Analysis (15 papers), Glaucoma and retinal disorders (14 papers) and Neural Networks and Applications (9 papers). José Sigut is often cited by papers focused on Retinal Imaging and Analysis (15 papers), Glaucoma and retinal disorders (14 papers) and Neural Networks and Applications (9 papers). José Sigut collaborates with scholars based in Spain, Slovenia and United Kingdom. José Sigut's co-authors include Francisco Fumero, Silvia Alayón, Marta González-Hernández, José Luis Sánchez de la Rosa, Rafael Arnay, L. Moreno, Carina Soledad González González, Evelio J. González, Manuel González de la Rosa and R.M. Aguilar and has published in prestigious journals such as Expert Systems with Applications, Computers & Education and IEEE Transactions on Biomedical Engineering.

In The Last Decade

José Sigut

44 papers receiving 951 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

RIM-ONE: An open retinal image database for optic nerve evaluation

2011 349 citations Francisco Fumero, Silvia Alayón et al. profile →

Peers

José Sigut

RNMI

OPHTH

CVPR

AI

MT

Tariq M. Khan Pakistan

Imran Qureshi Saudi Arabia

Yuta Nakashima Japan

Chunyu Hu China

Wan Mimi Diyana Wan Zaki Malaysia

Kashif Shaheed China

Aliaa A. A. Youssif Egypt

Ahmad Khan Pakistan

Daniela Raicu United States

Ömer Deperlioğlu Türkiye

Tariq M. Khan Pakistan

José Sigut

990 ×1.7

RNMI

705 ×1.4

OPHTH

936 ×2.3

CVPR

268 ×1.8

AI

75 ×1.0

MT

Citations per year, relative to José Sigut José Sigut (= 1×) peers Tariq M. Khan

Countries citing papers authored by José Sigut

Since Specialization

Citations

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

Fields of papers citing papers by José Sigut

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José Sigut

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Fumero, Francisco, et al.. (2025). Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry. Biomedical Physics & Engineering Express. 11(2). 25008–25008. 1 indexed citations

2.

Sigut, José, et al.. (2023). In-Depth Evaluation of Saliency Maps for Interpreting Convolutional Neural Network Decisions in the Diagnosis of Glaucoma Based on Fundus Imaging. Sensors. 24(1). 239–239. 5 indexed citations

3.

Sigut, José, et al.. (2023). Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis. Machine Learning Science and Technology. 4(4). 45024–45024. 5 indexed citations

4.

Sigut, José, et al.. (2023). Optic cup segmentation of stereo retinal fundus images using virtual reality. Multimedia Tools and Applications. 83(4). 9669–9683. 2 indexed citations

5.

Alayón, Silvia, et al.. (2023). Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images. Applied Sciences. 13(23). 12722–12722. 12 indexed citations

6.

Sigut, José, et al.. (2023). Fovea localization in retinal images using spatial color histograms. Multimedia Tools and Applications. 83(6). 17753–17771. 2 indexed citations

7.

González-Hernández, Marta, José Sigut, & Manuel González de la Rosa. (2017). Relationship between Retinal Nerve Fiber Layer Thickness and Hemoglobin Present in the Optic Nerve Head in Glaucoma. Journal of Ophthalmology. 2017. 1–10. 15 indexed citations

8.

BAMBO, M, Elena García‐Martín, José Sigut, et al.. (2013). Diagnostic ability of a new method for measuring haemoglobin levels in the optic nerve head in multiple sclerosis patients. British Journal of Ophthalmology. 97(12). 1543–1548. 13 indexed citations

9.

Sigut, José, et al.. (2010). Iris Center Corneal Reflection Method for Gaze Tracking Using Visible Light. IEEE Transactions on Biomedical Engineering. 58(2). 411–419. 65 indexed citations

10.

Sigut, José, et al.. (2008). A NEW SET OF FEATURES FOR ROBUST CHANGE DETECTION. 592–596. 3 indexed citations

11.

Castilla‐Rodríguez, Iván, L. Moreno, Carina Soledad González González, José Sigut, & Evelio J. González. (2007). SIMDE: An educational simulator of ILP architectures with dynamic and static scheduling. Computer Applications in Engineering Education. 15(3). 226–239. 3 indexed citations

12.

González, Evelio J., A. Hamilton, L. Moreno, et al.. (2007). Intelligent Agents and Apache Cocoon for a CV Generation System. 23. 9–15.

13.

Alayón, Silvia, et al.. (2006). An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution. Journal of Biomedical Informatics. 39(6). 573–588. 3 indexed citations

14.

Sigut, José, et al.. (2006). A neural network approach to normality testing. Intelligent Data Analysis. 10(6). 509–519. 3 indexed citations

15.

Alayón, Silvia, et al.. (2005). Cytological image analysis with a genetic fuzzy finite state machine. Computer Methods and Programs in Biomedicine. 80. S3–S15. 17 indexed citations

16.

Moreno, L., et al.. (2002). Towards a knowledge based system for the clinical evaluation of the electroencephalogram and visual evoked potentials. 4. 4069–4074. 1 indexed citations

17.

Moreno, L., et al.. (2001). Exploiting the advantages of symbolically interpretable continuous biomedical parameters with a fuzzyfied symbolic model. Artificial Intelligence in Medicine. 21(1-3). 253–262. 1 indexed citations

18.

Moreno, L., et al.. (2001). Tools for Acquisition, Processing and Knowledge-Based Diagnostic of the Electroencephalogram and Visual Evoked Potentials. Journal of Medical Systems. 25(3). 177–194. 3 indexed citations

19.

Moreno, L., et al.. (2000). Automatic analysis of signals with symbolic content. Artificial Intelligence in Medicine. 18(3). 245–265. 6 indexed citations

20.

Moreno, L., et al.. (1998). Evoked Potential Feature Detection with Recurrent Dynamic Neural Networks.. Natural Computing. 533–536. 2 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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