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 …

Data augmentation is one of the most effective techniques to improve the generalization
performance of deep neural networks. Yet, despite often facing limited data availability in …

Deep learning systems for medical image analysis have shown remarkable performance.
However performance is heavily dependent on the size and diversity of the training data as …

Data augmentation is a common method to make deep learning assessible on limited data
sets. However, classical image augmentation methods result in highly unrealistic images on …

Medical Ultrasound (US) is one of the most widely used imaging modalities in clinical
practice, but its usage presents unique challenges such as variable imaging quality. Deep …

Medical Ultrasound (US) is one of the most widely used imaging modalities in clinical
practice, but its usage presents unique challenges such as variable imaging quality. Deep …

Ultrasound imaging can vary in style/appearance due to differences in scanning equipment
and other factors, resulting in degraded segmentation and classification performance of …

In ultrasound imaging, sonographers are tasked with analyzing scans for diagnostic
purposes; a challenging task, especially for novice sonographers. Deep Learning methods …

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 …

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