White Matter Hyperintensities Segmentation with Prototype Learning

Alarcón Palomar, Óscar. (2020). White Matter Hyperintensities Segmentation with Prototype Learning Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.

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Título White Matter Hyperintensities Segmentation with Prototype Learning
Autor(es) Alarcón Palomar, Óscar
Abstract This paper proposes a new method -based on meta-learning- for the WMH Segmentation Challenge, organized by UMC Utrecht, VU Amsterdam, and NUHS Singapore hospitals. The purpose of this challenge is to compare methods for the semantic segmentation of white matter hyperintensities (WMH), which are brain white matter lesions, of presumably vascular origin in brain imaging obtained with magnetic resonance. White matter hyperintensities are found in patients with brain diseases like Parkinson, Alzheimer or stroke. Semantic segmentation refers to the process of linking each pixel in an image to a class label. The semantic segmentation of images has had a great advance with convolution neural networks, but they require a large number of images to be able to obtain good results. Convolutional neural networks are a type of neural networks specialized on images which architecture is similar to neurons’ pattern in human brain and they were inspired by the organization of the visual cortex. With the aim to reduce the number of images required in training, in this work, we propose the use of meta-learning algorithms, in particular prototype learning, to do this semantic segmentation. In addition, this approach also allows the network to be used in a different task for which it was not trained, which can improve its potential use. Results obtained suggest that it could be possible to use the network trained to a specific task (i.e., detect WMH in the brain), to another task (i.e. detect any kind of tumors in the brain).
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Palabra clave white matter hyperintensities
white matter lesions
meta learning
few-shot learning
prototype learning
semantic segmentation
convolutional neural network
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
Director/Tutor Santos Martín, Olga C.
Fecha 2020-10-06
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Oalarcon
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Oalarcon
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Wed, 22 Sep 2021, 18:18:19 CET