Publicaciones en las que colabora con Mateo Gende Lozano (10)
2024
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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, Vol. 37, Núm. 1, pp. 107-122
2023
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A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets
Medical and Biological Engineering and Computing, Vol. 61, Núm. 5, pp. 1093-1112
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Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios
IEEE Journal of Biomedical and Health Informatics, Vol. 27, Núm. 11, pp. 5483-5494
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Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning
Quantitative Imaging in Medicine and Surgery, Vol. 13, Núm. 5, pp. 2846-2859
2022
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End-to-end multi-task learning approaches for the joint epiretinal membrane segmentation and screening in OCT images
Computerized Medical Imaging and Graphics, Vol. 98
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High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2021
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Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches
IEEE Access, Vol. 9, pp. 75993-76004
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Automatic Segmentation and Visualisation of the Epirretinal Membrane in OCT Scans Using Densely Connected Convolutional Networks †
Engineering Proceedings, Vol. 7, Núm. 1