Mateo Gende

840 total citations
9 papers, 61 citations indexed

About

Mateo Gende is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mateo Gende has authored 9 papers receiving a total of 61 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Ophthalmology and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Mateo Gende's work include Retinal Imaging and Analysis (9 papers), Glaucoma and retinal disorders (6 papers) and Retinal Diseases and Treatments (5 papers). Mateo Gende is often cited by papers focused on Retinal Imaging and Analysis (9 papers), Glaucoma and retinal disorders (6 papers) and Retinal Diseases and Treatments (5 papers). Mateo Gende collaborates with scholars based in Spain and Netherlands. Mateo Gende's co-authors include Marcos Ortega, Joaquim de Moura, Jorge Novo, Elena García‐Martín, Clara I. Sá‎nchez, José Ignacio Fernández‐Vigo, J.M. Martínez-de-la-Casa and Manuel G. Penedo and has published in prestigious journals such as IEEE Access, IEEE Journal of Biomedical and Health Informatics and Medical & Biological Engineering & Computing.

In The Last Decade

Mateo Gende

9 papers receiving 61 citations

Peers

Mateo Gende

RNMI

OPHTH

BE

CVPR

AI

Savita Madhusudhan United Kingdom

Stefanos Apostolopoulos Switzerland

Nisarg A. Shah India

Moucheng Xu United Kingdom

Timothy J. Bennett United States

Wenbin Wei China

Xiaoting Wang China

Carlos Ciller Switzerland

Clarisa Sánchez Netherlands

Odysseas Georgiadis United Kingdom

Savita Madhusudhan United Kingdom

Mateo Gende

35 ×0.6

RNMI

44 ×1.5

OPHTH

12 ×0.7

BE

14 ×2.3

CVPR

7 ×1.4

AI

Citations per year, relative to Mateo Gende Mateo Gende (= 1×) peers Savita Madhusudhan

Countries citing papers authored by Mateo Gende

Since Specialization

Citations

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

Fields of papers citing papers by Mateo Gende

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mateo Gende

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

All Works

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9 of 9 papers shown

1.

Gende, Mateo, et al.. (2024). Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images. Journal of Imaging Informatics in Medicine. 37(1). 107–122. 5 indexed citations

2.

Gende, Mateo, Joaquim de Moura, José Ignacio Fernández‐Vigo, et al.. (2024). Circumpapillary OCT-based multi-sector analysis of retinal layer thickness in patients with glaucoma and high myopia. Computerized Medical Imaging and Graphics. 118. 102464–102464. 1 indexed citations

3.

Gende, Mateo, Joaquim de Moura, José Ignacio Fernández‐Vigo, et al.. (2023). Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning. Quantitative Imaging in Medicine and Surgery. 13(5). 2846–2859. 10 indexed citations

4.

Gende, Mateo, Joaquim de Moura, Jorge Novo, Manuel G. Penedo, & Marcos Ortega. (2023). A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets. Medical & Biological Engineering & Computing. 61(5). 1093–1112. 1 indexed citations

5.

Gende, Mateo, et al.. (2023). Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images. RUC (Universidade Da Coruña). 14. 77–73. 1 indexed citations

6.

Gende, Mateo, Joaquim de Moura, Elena García‐Martín, et al.. (2023). Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios. IEEE Journal of Biomedical and Health Informatics. 27(11). 5483–5494. 13 indexed citations

7.

Gende, Mateo, Joaquim de Moura, Jorge Novo, & Marcos Ortega. (2022). End-to-end multi-task learning approaches for the joint epiretinal membrane segmentation and screening in OCT images. Computerized Medical Imaging and Graphics. 98. 102068–102068. 18 indexed citations

8.

Gende, Mateo, et al.. (2021). Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches. IEEE Access. 9. 75993–76004. 11 indexed citations

9.

Gende, Mateo, et al.. (2021). Automatic Segmentation and Visualisation of the Epirretinal Membrane in OCT Scans Using Densely Connected Convolutional Networks. MDPI (MDPI AG). 2–2. 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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