Realistic Chest X-ray Image Synthesis via Generative Network with Stochastic Memristor Array for Machine Learning-based Medical Diagnosis

Abstract

Artificial Intelligence (AI) technology has attracted tremendous interest in the medical community, from image analysis to lesion diagnosis. However, progress in medical AI is hampered by a lack of available medical image datasets and labor-intensive labeling processes. Here, it is demonstrated that a large number of annotated, realistic chest X-ray images can be generated using a state-of-the-art generative adversarial network (GAN) that exploits noise produced by stochastic in-memory computing of memristor crossbar arrays. Memristors based on polymer film with high thermal resistance can increase the stochasticity of the tunneling distance for randomly ruptured conductive filaments via excessive Joule heating, thus generating true random numbers required for creating naturally diverse images in GAN. Using StyleGAN2-adaptive discriminator augmentation (ADA), high-quality chest X-ray images with and without pneumothorax are successfully augmented while maintaining a good Frechet inception distance score. The results provide a cost-effective solution for preparing privacy-sensitive medical images and labeling to develop innovative medical AI algorithms.