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Prédiction de la progression du myélome multiple par imagerie TEP : Adaptation des forêts de survie aléatoires et de réseaux de neurones convolutionnels

Abstract : The aim of this work is to provide a model for survival prediction and biomarker identification in the context of multiple myeloma (MM) using PET (Positron Emission Tomography) imaging and clinical data. This PhD is divided into two parts: The first part provides a model based on Random Survival Forests (RSF). The second part is based on the adaptation of deep learning to survival and to our data. The main contributions are the following: 1) Production of a model based on RSF and PET images allowing the prediction of a risk group for multiple myeloma patients. 2) Determination of biomarkers using this model.3) Demonstration of the interest of PET radiomics.4) Extension of the state of the art of methods for the adaptation of deep learning to a small database and small images. 5) Study of the cost functions used in survival. In addition, we are, to our knowledge, the first to investigate the use of RSFs in the context of MM and PET images, to use self-supervised pre-training with PET images, and, with a survival task, to fit the triplet cost function to survival and to fit a convolutional neural network to MM survival from PET lesions.
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Submitted on : Wednesday, May 25, 2022 - 4:25:11 PM
Last modification on : Saturday, June 25, 2022 - 3:53:11 AM

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  • HAL Id : tel-03678933, version 1

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Ludivine Morvan. Prédiction de la progression du myélome multiple par imagerie TEP : Adaptation des forêts de survie aléatoires et de réseaux de neurones convolutionnels. Bio-informatique [q-bio.QM]. École centrale de Nantes, 2021. Français. ⟨NNT : 2021ECDN0045⟩. ⟨tel-03678933⟩

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