A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
This work addresses the need for high-quality synthetic histopathology images in pathology for applications like education and data sharing, representing an incremental advance in domain-specific image generation.
The paper tackled the problem of generating synthetic histopathology images for brain cancer by introducing a diffusion probabilistic model with prioritized morphology weighting and color normalization, achieving superior performance compared to generative adversarial networks.
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.