Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation

IntroductionWhite matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementi...

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Detalles Bibliográficos
Autores principales: Kauê T. N. Duarte, Abhijot S. Sidhu, Murilo C. Barros, David G. Gobbi, Cheryl R. McCreary, Feryal Saad, Richard Camicioli, Eric E. Smith, Mariana P. Bento, Richard Frayne
Formato: Artículo
Lenguaje:English
Publicado: Frontiers Media S.A. 2024-10-01
Colección:Frontiers in Computational Neuroscience
Materias:
semi-supervised learning
convolutional neural networks (CNN)
U-Net
multi-stage learning
white matter hyperintensity (WMH)
Alzheimer's disease (AD)
Acceso en línea:https://www.frontiersin.org/articles/10.3389/fncom.2024.1487877/full

Internet

https://www.frontiersin.org/articles/10.3389/fncom.2024.1487877/full

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