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

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

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20 of 20 papers shown

#	Work	Indexed citations
1	Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry 2025 ·Biomedical Physics & Engineering Express ·Francisco Fumero, José Sigut, (unknown), (unknown)	1
2	In-Depth Evaluation of Saliency Maps for Interpreting Convolutional Neural Network Decisions in the Diagnosis of Glaucoma Based on Fundus Imaging 2023 ·Sensors ·José Sigut, Francisco Fumero, (unknown), Silvia Alayón, (unknown)	5
3	Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis 2023 ·Machine Learning Science and Technology ·José Sigut, Francisco Fumero, Rafael Arnay, (unknown), (unknown)	5
4	Optic cup segmentation of stereo retinal fundus images using virtual reality 2023 ·Multimedia Tools and Applications ·(unknown), (unknown), (unknown), José Sigut, Silvia Alayón, Francisco Fumero	2
5	Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images 2023 ·Applied Sciences ·Silvia Alayón, (unknown), Francisco Fumero, José Sigut, (unknown)	12
6	Fovea localization in retinal images using spatial color histograms 2023 ·Multimedia Tools and Applications ·José Sigut, (unknown), Francisco Fumero, Silvia Alayón, (unknown)	2
7	Relationship between Retinal Nerve Fiber Layer Thickness and Hemoglobin Present in the Optic Nerve Head in Glaucoma 2017 ·Journal of Ophthalmology ·Marta González-Hernández, José Sigut, Manuel González de la Rosa	15
8	Diagnostic ability of a new method for measuring haemoglobin levels in the optic nerve head in multiple sclerosis patients 2013 ·British Journal of Ophthalmology ·M BAMBO, Elena García‐Martín, (unknown), José Sigut, Francisco Fumero, (unknown), J.R. Ara, Jesús Martín, José M. Larrosa, Manuel González-de-la-Rosa	13
9	Iris Center Corneal Reflection Method for Gaze Tracking Using Visible Light 2010 ·IEEE Transactions on Biomedical Engineering ·José Sigut, (unknown)	65
10	A NEW SET OF FEATURES FOR ROBUST CHANGE DETECTION 2008 ·José Sigut, (unknown), (unknown), Carina Soledad González González	3
11	SIMDE: An educational simulator of ILP architectures with dynamic and static scheduling 2007 ·Computer Applications in Engineering Education ·Iván Castilla‐Rodríguez, L. Moreno, Carina Soledad González González, José Sigut, Evelio J. González	3
12	Intelligent Agents and Apache Cocoon for a CV Generation System 2007 ·Evelio J. González, A. Hamilton, L. Moreno, Juan Albino Méndez Pérez, G.N. Marichal, José Sigut, M. Sigut, (unknown)	0
13	An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution 2006 ·Journal of Biomedical Informatics ·Silvia Alayón, (unknown), José Sigut, José Luis Sánchez de la Rosa, Pedro Toledo	3
14	A neural network approach to normality testing 2006 ·Intelligent Data Analysis ·José Sigut, (unknown), (unknown), Pedro Toledo	3
15	Cytological image analysis with a genetic fuzzy finite state machine 2005 ·Computer Methods and Programs in Biomedicine ·(unknown), Silvia Alayón, L. Moreno, José Sigut, R.M. Aguilar	17
16	Towards a knowledge based system for the clinical evaluation of the electroencephalogram and visual evoked potentials 2002 ·L. Moreno, José Luis Sánchez de la Rosa, (unknown), (unknown), (unknown), José Sigut, R.M. Aguilar, (unknown), (unknown)	1
17	Exploiting the advantages of symbolically interpretable continuous biomedical parameters with a fuzzyfied symbolic model 2001 ·Artificial Intelligence in Medicine ·L. Moreno, (unknown), R.M. Aguilar, José Sigut, Carina Soledad González González	1
18	Tools for Acquisition, Processing and Knowledge-Based Diagnostic of the Electroencephalogram and Visual Evoked Potentials 2001 ·Journal of Medical Systems ·L. Moreno, José Luis Sánchez de la Rosa, (unknown), (unknown), (unknown), José Sigut, R.M. Aguilar, (unknown), (unknown)	3
19	Automatic analysis of signals with symbolic content 2000 ·Artificial Intelligence in Medicine ·L. Moreno, (unknown), R.M. Aguilar, José Luis Sánchez de la Rosa, José Sigut, (unknown), (unknown)	6
20	Evoked Potential Feature Detection with Recurrent Dynamic Neural Networks. 1998 ·Natural Computing ·(unknown), (unknown), L. Moreno, José Sigut, (unknown), R.M. Aguilar, José Luis Sánchez de la Rosa, (unknown)	2

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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