White Matter Hyperintensities Segmentation with Prototype Learning
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 brai...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/14590 |
| Acceso en línea: | https://hdl.handle.net/20.500.14468/14590 |
| Access Level: | acceso abierto |
| Palabra clave: | 1203.04 Inteligencia artificial white matter hyperintensities white matter lesions meta learning few-shot learning prototype learning semantic segmentation convolutional neural network |
| Sumario: | 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). |
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