  Recently deep learning methods, in particular, convolutional neural networks
(CNNs), have led to a massive breakthrough in the range of computer vision.
Also, the large-scale annotated dataset is the essential key to a successful
training procedure. However, it is a huge challenge to get such datasets in the
medical domain. Towards this, we present a data augmentation method for
generating synthetic medical images using cycle-consistency Generative
Adversarial Networks (GANs). We add semi-supervised attention modules to
generate images with convincing details. We treat tumor images and normal
images as two domains. The proposed GANs-based model can generate a tumor image
from a normal image, and in turn, it can also generate a normal image from a
tumor image. Furthermore, we show that generated medical images can be used for
improving the performance of ResNet18 for medical image classification. Our
model is applied to three limited datasets of tumor MRI images. We first
generate MRI images on limited datasets, then we trained three popular
classification models to get the best model for tumor classification. Finally,
we train the classification model using real images with classic data
augmentation methods and classification models using synthetic images. The
classification results between those trained models showed that the proposed
SAG-GAN data augmentation method can boost Accuracy and AUC compare with
classic data augmentation methods. We believe the proposed data augmentation
method can apply to other medical image domains, and improve the accuracy of
computer-assisted diagnosis.
