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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 successfultraining procedure. However, it is a huge challenge to get such datasets in themedical domain. Towards this, we present a data augmentation method forgenerating synthetic medical images using cycle-consistency GenerativeAdversarial Networks (GANs). We add semi-supervised attention modules togenerate images with convincing details. We treat tumor images and normalimages as two domains. The proposed GANs-based model can generate a tumor imagefrom a normal image, and in turn, it can also generate a normal image from atumor image. Furthermore, we show that generated medical images can be used forimproving the performance of ResNet18 for medical image classification. Ourmodel is applied to three limited datasets of tumor MRI images. We firstgenerate MRI images on limited datasets, then we trained three popularclassification models to get the best model for tumor classification. Finally,we train the classification model using real images with classic dataaugmentation methods and classification models using synthetic images. Theclassification results between those trained models showed that the proposedSAG-GAN data augmentation method can boost Accuracy and AUC compare withclassic data augmentation methods. We believe the proposed data augmentationmethod can apply to other medical image domains, and improve the accuracy ofcomputer-assisted diagnosis.

SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images