# Diffusion Models for Medical Image Analysis: A Comprehensive Survey

**Authors:** Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, Dorit Merhof  
**arXiv:** [2211.07804](https://arxiv.org/abs/2211.07804)  
**Date:** 2022-11-14  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2211.07804v1/

## Key points

- Denoising diffusion models are gaining attention in deep learning, especially in medical image analysis.
- These generative models perturb input data with Gaussian noise and then recover noise-free data through a reverse process.
- Diffusion models are known for their strong mode coverage and high-quality generated samples.
- The survey explores three main frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations in medical imaging.
- It categorizes diffusion models based on application, imaging modality, organ of interest, and algorithms.
- Diffusion models can generate synthetic images to address the scarcity of medical data for training deep neural networks.
- Case studies demonstrate the utility of using diffusion models to generate histopathology images with rare cancer subtypes.

## Summary

, , , , 

Denoising diffusion models have gained significant attention in deep learning, particularly in the field of medical image analysis. These generative models utilize a forward diffusion stage to gradually perturb input data with Gaussian noise and then reverse the process to recover noise-free data. Despite their computational complexity, diffusion models are praised for their strong mode coverage and high-quality generated samples. This comprehensive survey delves into the theoretical foundations and fundamental concepts of diffusion models, exploring three main frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. The survey provides a systematic taxonomy of diffusion models in medical imaging, categorizing them based on application, imaging modality, organ of interest, and algorithms. The survey covers various applications of diffusion models in medical imaging, highlighting practical use cases and discussing limitations within the field. It also proposes directions for future research to address these limitations effectively. Additionally, the survey includes an overview of studies with available open-source implementations to aid researchers in navigating the realm of diffusion models in medical imaging. Furthermore, the survey discusses how synthetic images generated by diffusion models can alleviate challenges related to scarcity of medical data for training deep neural networks. By generating synthetic samples using diffusion models, researchers can overcome the costly and time-consuming process of annotating medical images. The survey presents case studies demonstrating the utility of using diffusion models to generate histopathology images with rare cancer subtypes. Overall, this detailed survey provides a comprehensive overview of denoising diffusion models in medical image analysis. It showcases their potential across various applications such as image-to-image translation, segmentation, anomaly detection, reconstruction, generation, denoising, and other interdisciplinary applications. It highlights key contributions from different studies and sheds light on the novelty and significance of each approach presented in the literature.