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Deep learning for multi-site ms lesions segmentation: two-step intensity standardization and generalized loss function

Francesca Galassi 1 Solène Tarride 1 Emmanuel Vallée 2 Olivier Commowick 1 Christian Barillot 1
1 Empenn
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique , INSERM - Institut National de la Santé et de la Recherche Médicale
2 Orange Labs [Lannion]
Keywords : MS lesions MRI Segmentation CNN
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https://hal.archives-ouvertes.fr/hal-02052250
Contributor : Francesca Galassi <>
Submitted on : Thursday, February 28, 2019 - 1:21:20 PM
Last modification on : Tuesday, February 25, 2020 - 8:08:14 AM
Document(s) archivé(s) le : Wednesday, May 29, 2019 - 6:26:29 PM

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  • HAL Id : hal-02052250, version 1

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Francesca Galassi, Solène Tarride, Emmanuel Vallée, Olivier Commowick, Christian Barillot. Deep learning for multi-site ms lesions segmentation: two-step intensity standardization and generalized loss function. ISBI 2019 - 16th IEEE International Symposium on Biomedical Imaging, Apr 2019, Venice, Italy. pp.1. ⟨hal-02052250⟩

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