  We propose a novel method to improve deep learning model performance on
highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve
balanced dataset. We show that data augmentation with GAN helps to improve
accuracy of pneumonia binary classification task even if the generative network
was trained on the same training dataset.
