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Open AccessProceedings

Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction

1
CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, 17051 A Coruña, Spain
2
Department of Computer Science, Universidade da Coruña, 17051 A Coruña, Spain
*
Author to whom correspondence should be addressed.
Presented at 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 45; https://doi.org/10.3390/proceedings2019021045
Published: 7 August 2019
(This article belongs to the Proceedings of XoveTIC Conference)
PDF [293 KB, uploaded 7 August 2019]

Abstract

This work explores the use of paired and unpaired data for training deep neural networks in the multimodal reconstruction of retinal images. Particularly, we focus on the reconstruction of fluorescein angiography from retinography, which are two complementary representations of the eye fundus. The performed experiments allow to compare the paired and unpaired alternatives.
Keywords: deep learning; generative adversarial networks; eye fundus; multimodal deep learning; generative adversarial networks; eye fundus; multimodal
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hervella, Á.; Rouco, J.; Novo, J.; Ortega, M. Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction. Proceedings 2019, 21, 45.

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