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

A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images