Applications and Implementation of Diffusion Models in Medical Image Processing

Abstract

Many of today’s widely used image generation AIs utilize Diffusion Models for image synthesis. Unlike traditional generative models, Diffusion Models offer more stable training and the ability to produce high-quality images. As a result, their use has extended beyond natural images to the field of medical image processing, where various methods leveraging Diffusion Models for medical image generation and segmentation have been proposed. To conduct cutting-edge research, it is essential to understand the trends surrounding Diffusion Models. This paper provides an overview of the mechanisms of Diffusion Models, introduces their applications in medical image generation and segmentation, and presents examples of their implementation.