Biomedical image augmentation using Augmentor

Marcus Daniel Bloice, Peter M. Roth, Andreas Holzinger

Research output: Contribution to journalArticleResearchpeer-review

Abstract


Motivation
Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognised due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed.
Results
Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomised elastic distortions. The software has been designed to be highly extensible, meaning an operation that might be …
Original languageEnglish
Pages (from-to)4522-4524
JournalBioinformatics
Volume35
Issue number21
DOIs
Publication statusPublished - 2019

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Augmentation
Biomedical Imaging
Software
Imaging techniques
Learning
Software packages
Computer vision
Pipelines
Software Package
Computer Vision
Neural Networks

Cite this

Biomedical image augmentation using Augmentor. / Bloice, Marcus Daniel; Roth, Peter M.; Holzinger, Andreas.

In: Bioinformatics, Vol. 35, No. 21, 2019, p. 4522-4524.

Research output: Contribution to journalArticleResearchpeer-review

Bloice, Marcus Daniel ; Roth, Peter M. ; Holzinger, Andreas. / Biomedical image augmentation using Augmentor. In: Bioinformatics. 2019 ; Vol. 35, No. 21. pp. 4522-4524.
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