In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.
For example, Adar et al. successfully increased classification performance when using GANs to enlarge training data size and diversity for liver lesion classification REF .
Synthetic data augmentation using GAN for improved liver lesion classification
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Computer Science