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MIC-DKFZ/basic_unet_example: Release v2019.01

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 29 Jan 2019Publisher:Zenodo
Authors: Klein, André; Wasserthal, Jakob; Greiner, Matthias; Zimmerer, David; Maier-Hein, Klaus H.;
Code repository: https://github.com/MIC-DKFZ/basic_unet_example/tree/v2019.01

doi: 10.5281/zenodo.2552439 , 10.5281/zenodo.2549508

MIC-DKFZ/basic_unet_example: Release v2019.01

Abstract

This python package is an example project of how to use a U-Net (Ronneberger et al.) for segmentation on medical images using PyTorch (https://www.pytorch.org). It was developed at the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). It is also an example on how to use our other python packages batchgenerators (https://github.com/MIC-DKFZ/batchgenerators) and Trixi (https://github.com/MIC-DKFZ/trixi) to suit all our deep learning data augmentation needs.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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