Synthetic Chest X-ray Image Generation Technology

This whitepaper presents a comprehensive exploration of synthetic chest x-ray image generation, focusing on the challenges and solutions in the medical imaging field. It begins by addressing the critical problem of data scarcity and privacy concerns that hinder the development of robust Computer Aided Diagnosis (CADx) systems. The proposed solution utilizes the DiffuseVAE architecture, which integrates Variational Autoencoders (VAEs) and Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic images. The document details the image generation module, which includes encoder model training and diffusion model training, and emphasizes the importance of representation learning to enhance image synthesis. Additionally, it discusses the incorporation of a self-supervised learning framework based on Momentum Contrastive Learning (MoCo) to improve image representation quality. The paper concludes with insights into the evaluation of the solution and its potential impact on medical imaging, highlighting the necessity for diverse and realistic synthetic images to aid in medical diagnostics.