Medical Image Synthetic Data Augmentation Using GAN
Huijuan Zhang, Zongrun Huang, Zhongwei Lv · Proceedings of the 4th International Conference on Computer Science and Application Engineering · 2020
Most medical image datasets are limited and unbalanced. The classification method based on deep neural networks is prone to under-fitting or over-fitting problems on such data sets, which affects the final classification performance. This paper proposes a synthetic data augmentation method based on progressive generative adversarial network, which aims to solve the problem that deep neural networks are difficult to train on small-scale medical image datasets. Experimental results show that the data synthesized by this method is better than existing methods. The classification performance using only classic data augmentation yielded 76.8% sensitivity and 88.4% specificity. By applying the synthetic data augmentation, the results significantly increased to 84.2% sensitivity and 92.1% specificity.