A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

TL;DR

This paper introduces a diffusion probabilistic model tailored for synthesizing high-quality histopathology images, emphasizing morphology and color normalization, with potential applications in medical education and data sharing.

Contribution

The study is the first to apply diffusion probabilistic models with morphology weighting and color normalization for histopathology image synthesis, outperforming GANs.

Findings

Diffusion models can generate diverse histopathology images.

The proposed method outperforms GAN-based approaches.

High-quality images suitable for medical applications.

Abstract

Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.