Mateo Gende

Publication Activity (10 Years)

Years Active: 2021-2023
Publications (10 Years): 8

Top Topics

Optical Coherence Tomography

Automatic Segmentation

Myelinated Nerve

Top Venues

IEEE J. Biomed. Health Informatics

Medical Biol. Eng. Comput.

Publications

Mateo Gende, Joaquim de Moura, Jorge Novo, Manuel G. Penedo, Marcos Ortega
A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets. Medical Biol. Eng. Comput. 61 (5) (2023)
Mateo Gende, Víctor Mallen, Joaquim de Moura, Beatriz Cordón, Elena Garcia-Martin, Clara I. Sánchez, Jorge Novo, Marcos Ortega
Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios. IEEE J. Biomed. Health Informatics 27 (11) (2023)
Mateo Gende, Joaquim de Moura, Jorge Novo, Manuel F. González Penedo, Marcos Ortega
Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images. XoveTIC (2022)
Mateo Gende, Daniel Iglesias Morís, Joaquim de Moura, Jorge Novo, Marcos Ortega
Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images. EUROCAST (2022)
Mateo Gende, Joaquim de Moura, Jorge Novo, Marcos Ortega
End-to-end multi-task learning approaches for the joint epiretinal membrane segmentation and screening in OCT images. Comput. Medical Imaging Graph. 98 (2022)
Daniel Iglesias Morís, Mateo Gende, Joaquim de Moura, Jorge Novo, Marcos Ortega
Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening. EUROCAST (2022)
Mateo Gende, Joaquim de Moura, Jorge Novo, Marcos Ortega
High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks. ICIAP (1) (2022)
Mateo Gende, Joaquim de Moura, Jorge Novo, Pablo Charlón, Marcos Ortega
Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches. IEEE Access 9 (2021)