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Apprentissage profond pour la segmentation et la détection automatique en imagerie multi-modale : application à l'oncologie hépatique

Abstract : In order to characterize hepatic lesions,radiologists rely on several images using different modalities (different MRI sequences, CT scan, etc.) because they provide complementary information.In addition, automatic segmentation and detection tools are a great help in characterizing lesions, monitoring disease or planning interventions.At a time when deep learning dominates the state of the art in all fields related to medical image processing, this thesis aims to study how these methods can meet certain challenges related to multi-modal image analysis, revolving around three axes : automatic segmentation of the liver, the interpretability of segmentation networks and detection of hepatic lesions.Multi-modal segmentation in a context where the images are paired but not registered with respect to each other is a problem that is little addressed in the literature.I propose a comparison of learning strategies that have been proposed for related problems, as well as a method to enforce a constraint of similarity of predictions into learning.Interpretability in machine learning is a young field of research with particularly important issues in medical image processing, but which so far has focused on natural image classification networks.I propose a method for interpreting medical image segmentation networks.Finally, I present preliminary work on a method for detecting liver lesions in pairs of images of different modalities.
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Submitted on : Thursday, July 15, 2021 - 10:24:11 AM
Last modification on : Tuesday, October 19, 2021 - 11:14:15 AM
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Vincent Couteaux. Apprentissage profond pour la segmentation et la détection automatique en imagerie multi-modale : application à l'oncologie hépatique. Imagerie médicale. Institut Polytechnique de Paris, 2021. Français. ⟨NNT : 2021IPPAT009⟩. ⟨tel-03286740⟩

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