Image augmentation is a commonly performed technique to prevent class imbalance in
datasets to compensate for insufficient training samples, or to prevent model overfitting …

Medical imaging datasets are limited in size due to privacy issues and the high cost of
obtaining annotations. Augmentation is a widely used practice in deep learning to enrich the …

A common problem in computer vision--particularly in medical applications--is a lack of
sufficiently diverse, large sets of training data. These datasets often suffer from severe class …

Medical image datasets are usually imbalanced due to the high costs of obtaining the data
and time-consuming annotations. Training a deep neural network model on such datasets to …

Data augmentation, also called implicit regularization, is one of the popular strategies to
improve the generalization capability of deep neural networks. It is crucial in situations …

Recent works show that Generative Adversarial Networks (GANs) can be successfully
applied to chest X-ray data augmentation for lung disease recognition. However, the …

Chest radiography is the most common radiographic examination performed in daily clinical
practice for the detection of various heart and lung abnormalities. The large amount of data …

In medical images, data augmentation is essentially important for accurate classification of
images especially when available data is limited. This paper proposes a noble data …

Successful training of convolutional neural networks (CNNs) requires a substantial amount
of data. With small datasets, networks generalize poorly. Data Augmentation techniques …

Abstract Deep learning, via Convolutional Neural Network (CNN) models, has had
significant breakthroughs and achievements in image classification tasks where there is a …