This repo contains the code for: Generative Modeling of Neutrophil Behavior.
Neutrophil are highly motile white blood cells in most mammals. Their motion and appearance have a strong correlation with cell cycle and disease progression. In this work, we propose an aggregate model that combine Generative Adversarial Networks (GANs) and Autoregressive (AR) models to predict cell motion and appearance in human neutrophils imaged by differential interference contrast microscopy. We bifurcate the task of learning cell statistics by leveraging GANs for the spatial component and AR models for the temporal component.
You can find the code related to main components of this work: GANs and AR in their respective folders.