An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

Abstract

The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and $F1$ score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.