Biomedical image augmentation using Augmentor

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 recognized 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 randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain. Availability and implementation Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager (A Julia version of the package, developed in parallel by Christof Stocker, is also available under https://github.com/Evizero/Augmentor.jl).

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

“…Our experiments showed that increasing the dataset up to 30 000 images per category helped to achieve good results. Special care was taken to increase the dataset by using the Augmentor library [49] which permits to rotate images avoiding excessive distortion. Compared to our previous work, the image pre-processing has also been changed: instead of using histogram equalization, CLAHE was selected to enhance the contrast the images.…”

“…Because of that, some researchers use right angles. In our case, we have used the Augmentor Library [49], which has been designed to permit rotations of the images limiting the degree of distortion. Additionally, the Augmentor library permits to apply other operations for data augmentation like zoom, bright, shear.…”

“…Regarding software resources, our program used Tensorflow v13.1 [54] as the machine learning framework. For image data augmentation, as indicated before, the Augmentor library [49] was used.…”

Architecture Recognition by Means of Convolutional Neural Networks

“…operations: Data augmentation resulted in more than 70% duplicate images for each classes, so we needed to generate more than the targeted 600 images. In our experiment, we produced around 2000 images per class using Augmentor (Bloice et al, 2019) (image augmentation library in Python). The data cleaning was done using a MongoDB 2 database to hash the images.…”

AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

“…Segmentation annotations are converted to bounding boxes. As for the data augmentation purpose, we apply random rotation, zoom crop/expand, horizontal/vertical flip and distortions by using Augmentor [6]. In the end, we obtain a training set of 25K images.…”