GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

Understand

Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets.

• However, obtaining such datasets in the medical domain remains a challenge.
• In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs).
• Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification.