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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141427" target="_blank" >RIV/00216305:26220/21:PU141427 - isvavai.cz</a>
Alternative codes found
RIV/65269705:_____/21:00074507
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-76423-4_10" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-76423-4_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-76423-4_10" target="_blank" >10.1007/978-3-030-76423-4_10</a>

Result language
angličtina
Original language name
On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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