Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review

TL;DR

This paper reviews recent advances in diffusion models focusing on computational efficiency and their applications in medical imaging, highlighting models like DDPM, LDM, and WDM and discussing future research directions.

Contribution

It categorizes and analyzes recent diffusion models, emphasizing their efficiency improvements and applications in medical imaging, and discusses current limitations and future opportunities.

Findings

Diffusion models have been categorized into DDPM, LDM, and WDM.

Recent advances have improved efficiency and reduced inference time.

Challenges remain in computational complexity and model limitations.

Abstract

The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models have been successfully applied across a range of applications. However, a significant challenge remains with the high computational cost associated with training and generating these models. This study focuses on the efficiency and inference time of diffusion-based generative models, highlighting their applications in both natural and medical imaging. We present the most recent advances in diffusion models by categorizing them into three key models: the Denoising Diffusion Probabilistic Model (DDPM), the Latent Diffusion Model (LDM), and the Wavelet Diffusion Model (WDM). These models play a crucial role in medical imaging, where producing fast, reliable, and high-quality medical images is essential for accurate analysis of abnormalities and disease diagnosis. We first investigate the general framework of DDPM, LDM, and WDM and discuss the computational complexity gap filled by these models in natural and medical imaging. We then discuss the current limitations of these models as well as the opportunities and future research directions in medical imaging.