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KOLAŘÍK, M.; BURGET, R.; TRAVIESO-GONZÁLEZ, C.; KOČICA, J.
Original Title
On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
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.
English abstract
Keywords
Multiple sclerosis; Reproducibility; Segmentation; transfer learning
Key words in English
Authors
RIV year
2022
Released
14.05.2021
Publisher
Springer, Cham
Location
Online
ISBN
978-3-030-76422-7
Book
Reproducible Research in Pattern Recognition
Edition
Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers
Pages from
146
Pages to
151
Pages count
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" }