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<dc:title xml:lang="fr">Augmentation de données pour l'analyse d'images histopathologiques : approches par génération d'images et déformations spatiales pour la segmentation de glomérules</dc:title>
<dcterms:alternative xml:lang="en">Data augmentation for histopathological images analysis : approaches by image generation and random spatial déformations for glomeruli segmentation</dcterms:alternative>
<dc:subject xml:lang="fr">Histopathologie numérique</dc:subject>
<dc:subject xml:lang="fr">Glomérules</dc:subject>
<dc:subject xml:lang="fr">Segmentation</dc:subject>
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<dc:subject xml:lang="fr">Augmentation de données</dc:subject>
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<dc:subject xml:lang="fr">Synthèse de texture</dc:subject>
<dc:subject xml:lang="en">Digital histopathology</dc:subject>
<dc:subject xml:lang="en">Glomeruli</dc:subject>
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<tef:elementdEntree autoriteExterne="029614996" autoriteSource="Sudoc">Glomérule du rein</tef:elementdEntree>
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<dcterms:abstract xml:lang="fr">Dans le cadre de cette thèse, nous nous intéressons à des données histopathologiques rénales, et en particulier à la segmentation de glomérules. Ces structures sont complexes et comportent de multiples sous-structures rendant leur segmentation automatique particulièrement difficile. Notre objectif est d'améliorer la segmentation automatique de glomérules dans des coupes complètes en utilisant un CNN de type U-Net. L'entraînement d'un tel modèle nécessite une grande quantité d'images annotées. Or, dans notre contexte, le nombre d'images annotées disponibles est de l'ordre de quelques centaines seulement, ce qui pose la question des augmentations de données. Nous proposons d'étudier l'application et l'impact d'augmentations de deux types. Nous étudions premièrement les variations géométriques, introduites à l'aide de déformations spatiales aléatoires. Deuxièmement, nous étudions les variations de texture, introduites à l'aide de méthodes de synthèse de texture et de modèles génératifs.</dcterms:abstract>
<dcterms:abstract xml:lang="en">In this thesis, we are interested in renal histopathological data and in particular glomeruli segmentation. These structures are complex and include multiple substructures making their automatic segmentation particularly difficult. Our objective is to improve the automatic segmentation of glomeruli in whole slide images using a CNN called U-Net, a standard model in medical image segmentation. Training such a model requires a large amount of annotated images (several tens of thousands). However, in our context, the number of available annotated images is of the order of a few hundreds, which raises the question of data augmentation. This thesis investigates the application and the impact of two types of augmentation techniques. We first study geometric variations, introduced using random spatial deformations. Second, we study texture variations, introduced using texture synthesis methods and generative models.</dcterms:abstract>
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