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DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

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DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

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Kamraoui, RA.; Ta, V.; Tourdias, T.; Mansencal, B.; Manjón Herrera, JV.; Coupé, P. (2022). DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation. Medical Image Analysis. 76:1-13. https://doi.org/10.1016/j.media.2021.102312

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/199575

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Title: DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation
Author: Kamraoui, Reda Abdellah Ta, Vinh-Thong Tourdias, Thomas Mansencal, Boris Manjón Herrera, José Vicente Coupé, Pierrick
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Issued date:
Abstract:
[EN] Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising per-formance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even out-performed human ...[+]
Subjects: Multiple Sclerosis Segmentation , Deep Learning , Domain Generalization , MRI , Segmentation
Copyrigths: Cerrado
Source:
Medical Image Analysis. (issn: 1361-8415 )
DOI: 10.1016/j.media.2021.102312
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.media.2021.102312
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-87743-R/ES/DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICAS/
info:eu-repo/grantAgreement/ANR//ANR-10-LABX-57/
info:eu-repo/grantAgreement/ANR//ANR-18-CE45-0013/
info:eu-repo/grantAgreement/Université de Bordeaux//ANR-10-IDEX-03-02//Initiative of Excellence/
Thanks:
This work benefited from the support of the project Deep-volBrain of the French National Research Agency (ANR-18-CE45-0013) . This study was achieved within the context of the Labo-ratory of Excellence TRAIL ANR-10-LABX-57 ...[+]
Type: Artículo

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