Detail publikace

On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

KOLAŘÍK, M. BURGET, R. TRAVIESO-GONZÁLEZ, C. KOČICA, J.

Originální název

On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This article describes detailed notes on the practical implementation of our paper Planar 3D transfer learning for end to end unimodal MRI unbalanced data segmentation (ICPR 2020, Milan), which deals with a problem of multiple sclerosis lesion segmentation from a unimodal MRI flair brain scan by applying a planar 3D transfer learning backbone weights to an autoencoder segmentation neural network. Our source code is published online under an open-source license, and we provide step-by-step instructions for the reproduction of our results.

Klíčová slova

Multiple sclerosis; Reproducibility; Segmentation; transfer learning

Autoři

KOLAŘÍK, M.; BURGET, R.; TRAVIESO-GONZÁLEZ, C.; KOČICA, J.

Vydáno

14. 5. 2021

Nakladatel

Springer, Cham

Místo

Online

ISBN

978-3-030-76422-7

Kniha

Reproducible Research in Pattern Recognition

Edice

Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers

Strany od

146

Strany do

151

Strany počet

7

URL

https://link.springer.com/chapter/10.1007/978-3-030-76423-4_10

BibTex

@inproceedings{BUT172287,
  author="KOLAŘÍK, M. and BURGET, R. and TRAVIESO-GONZÁLEZ, C. and KOČICA, J.",
  title="On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation",
  booktitle="Reproducible Research in Pattern Recognition",
  year="2021",
  series="Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers",
  pages="146--151",
  publisher="Springer, Cham",
  address="Online",
  doi="10.1007/978-3-030-76423-4\{_}10",
  isbn="978-3-030-76422-7",
  url="https://link.springer.com/chapter/10.1007/978-3-030-76423-4_10"
}