Unsupervised Histopathology Image Synthesis

Understand

Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer.

• Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists.
• In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks.
• We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.