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Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images

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http://hdl.handle.net/2183/36401
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  • Investigación (FIC) [1728]
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Title
Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images
Author(s)
Gende, M.
Moura, Joaquim de
Penedo, Manuel
Novo Buján, Jorge
Ortega Hortas, Marcos
Date
2023-02-16
Citation
M. Gende, J. D. Moura, J. Novo, M. F. González Penedo, y M. Ortega, «Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images», In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, Kalpa Publications in Computing, vol 14, pp. 77-73. doi: 10.29007/xxh7.
Abstract
[Absctract]: The Epiretinal Membrane (ERM) is an ocular pathology that causes visual distortion. In order to detect and treat the ERM, ophthalmologists visually inspect Optical Coherence Tomography (OCT) images.This is a costly and subjective process. In this work, we present three different fully automatic, end-to-end approaches that make use of multi-task learning to simultaneously screen for and segment ERM symptoms in OCT images. These approaches were implemented into three architectures that capitalise on the way the models share a single architecture for the two complementary tasks.
Keywords
Deep learning
Epiretinal membrane
Multi-task learning
Optical Coherence Tomography
 
Description
V Congreso XoveTIC, organizado por el Centro de Investigación en TIC da Universidade da Coruña (CITIC), 5 y 6 de octubre de 2022, A Coruña.
Editor version
https://doi.org/10.29007/xxh7
ISSN
2515-1762

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