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 …

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 …

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 …

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 …

The limited availability of data poses a challenge for the effective application of deep
learning techniques. To address this, various data augmentation methods have been …

Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …

Deep Learning brings back today valuable answers to many issues relating to different
areas of life. To maximize the performance of a deep learning model, the model must be …

Breast classification and detection using ultrasound imaging is considered a significant step
in computer-aided diagnosis systems. Over the previous decades, researchers have proved …

Data augmentation is widely utilized to achieve more generalizable and accurate deep
learning models based on relatively small labeled datasets. Data augmentation techniques …