Abstract
Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability to process complex data, extract valuable information, and enhance image quality. This review provides an overview of diffusion models in microscopic images and micro-alike images, focusing on three commonly used models: DDPM, DDIM, and SDEs. We explore their applications in image generation, segmentation, denoising, classification, reconstruction and super-resolution. It shows their notable advantages, particularly in image generation and segmentation. Through simulating the imaging process of biological samples under the microscope, diffusion model can generate high-quality synthetic microscopic images. The generated images serve as a powerful tool for data augmentation when training deep learning models. Diffusion model also excels in microscopic image segmentation. It enables to accurately segment different cellular regions and tissue structures by simulating the interactions between pixels in an image. The review includes 31 papers, with 13 on image generation, nine on segmentation, and the remainder on other applications. We also discuss the strengths, limitations, and future directions for diffusion models in biomedical image processing.