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White Matter Hyperintensities Segmentation with Prototype Learning

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dc.contributor.authorAlarcón Palomar, Óscar
dc.contributor.directorSantos, Olga C.
dc.date.accessioned2024-05-20T12:36:06Z
dc.date.available2024-05-20T12:36:06Z
dc.date.issued2020-10-06
dc.description.abstractThis 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).en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14590
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordswhite matter hyperintensities
dc.subject.keywordswhite matter lesions
dc.subject.keywordsmeta learning
dc.subject.keywordsfew-shot learning
dc.subject.keywordsprototype learning
dc.subject.keywordssemantic segmentation
dc.subject.keywordsconvolutional neural network
dc.titleWhite Matter Hyperintensities Segmentation with Prototype Learninges
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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