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

3 pages•Published: February 16, 2023

Mateo Gende, Joaquim de Moura, Jorge Novo, Manuel F. González Penedo and Marcos Ortega

Abstract

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.

Keyphrases: computer aided diagnosis, deep learning, epiretinal membrane, multi task learning, optical coherence tomography

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 77-79.

Links:	https://easychair.org/publications/paper/D8s6
	https://doi.org/10.29007/xxh7

BibTeX entry

@inproceedings{XoveTIC2022:Multi_task_Convolutional_Neural,
  author    = {Mateo Gende and Joaquim de Moura and Jorge Novo and Manuel F. González Penedo and Marcos Ortega},
  title     = {Multi-task Convolutional Neural Networks for the End-to-end Simultaneous Segmentation and Screening of the Epiretinal Membrane in OCT Images},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/D8s6},
  doi       = {10.29007/xxh7},
  pages     = {77-79},
  year      = {2023}}

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