Data augmentation is a critical regularization technique for deep neural networks,
particularly in medical image classification. Popular data augmentation approaches include …

Data augmentation is a crucial regularization technique for deep neural networks,
particularly in medical imaging tasks with limited data. Deep learning models are highly …

Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Supervised deep learning methods for segmentation require large amounts of labelled
training data, without which they are prone to overfitting, not generalizing well to unseen …

Data augmentation is an essential part of training discriminative Convolutional Neural
Networks (CNNs). A variety of augmentation strategies, including horizontal flips, random …

In this paper we propose a novel augmentation technique that improves not only the
performance of deep neural networks on clean test data, but also significantly increases …

In recent years, data augmentation has advanced to the point where it no longer relies on
traditional photometric or geometric image processing techniques, such as rotation, scale …

Supervised training of an automated medical image analysis system often requires a large
amount of expert annotations that are hard to collect. Moreover, the proportions of data …

Data augmentation is a widely used and effective technique to improve the generalization
performance of deep neural networks. Yet, despite often facing limited data availability when …

Deep learning methods develop very rapidly and are widely used in computer vision
applications as well as for medical image analysis. The deep learning methods provide a …