Histopathology Image Synthesis

Histopathology image synthesis aims to generate realistic microscopic images of tissue samples, primarily to address the scarcity of annotated data for training deep learning models in digital pathology. Current research heavily utilizes diffusion models, often coupled with vision transformers or autoencoders, to synthesize images with accurate nuclei boundaries and diverse tissue textures, sometimes incorporating style control or multi-attribute generation. This technology significantly impacts the field by enabling the creation of larger, more diverse training datasets for improved diagnostic algorithms and potentially accelerating the development of AI-assisted pathology tools.