Skip to Main content Skip to Navigation
Conference papers

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
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Orange Labs [Lannion]
Keywords : MS lesions MRI Segmentation CNN
Document type :
Conference papers
Complete list of metadata
https://hal.archives-ouvertes.fr/hal-02052250
Contributor : Francesca Galassi <>
Submitted on : Thursday, February 28, 2019 - 1:21:20 PM
Last modification on : Friday, January 15, 2021 - 3:33:20 AM
Long-term archiving on: : Wednesday, May 29, 2019 - 6:26:29 PM

File

Vignette du document
Template_ISBI2019.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02052250, version 1

Collections

CNRS | UNIV-RENNES1 | CENTRALESUPELEC | IRISA | INRIA | UNIV-RENNES | UR1-UFR-ISTIC | INSERM | IRISA-INSA-R | INSA-RENNES

Citation

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⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

400

Files downloads

285