An Exploration of Diffusion Models in the Context of Data Augmentation

This survey paper examines the transformative role of medical imaging in healthcare and the integration of AI systems for diagnosis and treatment. The emergence of HealthTech startups and AI algorithms highlights the industry's rapid evolution. However, challenges persist in obtaining sensitive medical data due to consent and privacy concerns. The survey paper orchestrates an exhaustive exploration of potential remedies, harnessing the capabilities of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the innovative Stable Diffusion architecture. These cutting-edge frameworks collectively facilitate the synthesis of highly authentic medical images, adeptly alleviating the challenges tied to data procurement. In parallel, the survey paper undertakes a meticulous comparative analysis underpinned by three pivotal benchmarks: sampling efficiency, image diversity, and the overarching quality of the generated images.