Generating realistic blood-cell images using cycle-consistent generative adversial networks

Abstract

generative adversial networks are a neural-network based generative models, predominantly used for generating data-samples close to the data distribution they have been trained on. A model for generating realistic blood cell images based on cycle-consistent generative adversial networks is developed along with their corresponding segmentation masks.