Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction

  1. Álvaro S. Hervella 1
  2. José Rouco
  3. Jorge Novo 1
  4. Marcos Ortega 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Libro:
XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September
  1. Alberto Alvarellos González (ed. lit.)
  2. José Joaquim de Moura Ramos (ed. lit.)
  3. Beatriz Botana Barreiro (ed. lit.)
  4. Javier Pereira Loureiro (ed. lit.)
  5. Manuel F. González Penedo (ed. lit.)

Editorial: MDPI

ISBN: 978-3-03921-444-0,  978-3-03921-443-3

Ano de publicación: 2019

Congreso: XoveTIC (2. 2019. A Coruña)

Tipo: Achega congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Resumo

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.

Fonte de datos: Dialnet