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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Metadatos
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Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT ImagesData
2023-02-16Cita bibliográfica
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.
Resumo
[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.
Palabras chave
Deep learning
Epiretinal membrane
Multi-task learning
Optical Coherence Tomography
Epiretinal membrane
Multi-task learning
Optical Coherence Tomography
Descrición
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.
Versión do editor
ISSN
2515-1762