On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
| Autor: | Martin Kolarik, Radim Burget, Carlos M. Travieso-González, Jan Kočica |
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| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
Source code Artificial neural network Computer science business.industry media_common.quotation_subject Pattern recognition Autoencoder 03 medical and health sciences 0302 clinical medicine Planar End-to-end principle Segmentation Artificial intelligence Unbalanced data Transfer of learning business 030217 neurology & neurosurgery 030304 developmental biology media_common |
| Zdroj: | Reproducible Research in Pattern Recognition ISBN: 9783030764227 RRPR |
| DOI: | 10.1007/978-3-030-76423-4_10 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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