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Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

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Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

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Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Pérez-Pelegrí, L.; Maceira, AM.; Bodi, V.; Moratal, D. (2021). Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology. Computer Methods and Programs in Biomedicine. 208:1-8. https://doi.org/10.1016/j.cmpb.2021.106275

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/181741

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Title: Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology
Author: Pérez-Pelegrí, Manuel Monmeneu, Jose V. López-Lereu, María P. Pérez-Pelegrí, Lucía Maceira, Alicia M. Bodi, Vicente Moratal, David
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies ...[+]
Subjects: Magnetic resonance imaging , Deep learning , Left ventricle , Weak supervision , Explainability , Segmentation
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Computer Methods and Programs in Biomedicine. (issn: 0169-2607 )
DOI: 10.1016/j.cmpb.2021.106275
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.cmpb.2021.106275
Project ID:
info:eu-repo/grantAgreement/MCIU//CIIP-20192020/
info:eu-repo/grantAgreement/CDTI//E9113//EUROSTARS project/
info:eu-repo/grantAgreement/AVI//INNCAD00%2F19%2F085//Proyecto 4DTools: nuevas técnicas y biomarcadores para diagnóstico-pronóstico de patologías de la aorta ascendente a través de técnicas de imagen médica/
info:eu-repo/grantAgreement/GVA//AEST%2F2019%2F037/
info:eu-repo/grantAgreement/GVA//AEST%2F2020%2F029//Aplicación de técnicas de deep learning (aprendizaje profundo) para un análisis automático de imágenes de Resonancia/
Thanks:
The authors acknowledge financial support from the Consel-leria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029) , from the Agencia Valenciana de la Innovacion, ...[+]
Type: Artículo

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