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

• URL: http://arxiv.org/abs/2011.07534v1
• Date: Sun, 15 Nov 2020 14:01:24 GMT
• Title: SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
• Authors: Chang Qi, Junyang Chen, Guizhi Xu, Zhenghua Xu, Thomas Lukasiewicz, Yang Liu
• Abstract summary: We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) 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. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
• Score: 47.35184075381965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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