Synthetic Chest X Ray

Synthetic chest X-ray (CXR) image generation is a rapidly developing field aiming to address the limitations of real CXR datasets, such as scarcity, bias, and privacy concerns. Current research focuses on creating realistic synthetic CXRs using generative models like GANs, diffusion models, and variations of the Segment-Anything Model, often coupled with language models for fine-grained control and annotation. These synthetic images are used for data augmentation, improving the performance of diagnostic models and enabling research into new multimodal applications involving paired CT scans and various X-ray views. The resulting advancements have significant implications for improving the accuracy and accessibility of CXR-based disease detection and diagnosis.