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Cell motion and appearance have a strong correlation with cell cycle anddisease progression. Many contemporary efforts in machine learning utilizespatio-temporal models to predict a cell's physical state and, consequently,the advancement of disease. Alternatively, generative models learn theunderlying distribution of the data, creating holistic representations that canbe used in learning. In this work, we propose an aggregate model that combineGenerative Adversarial Networks (GANs) and Autoregressive (AR) models topredict cell motion and appearance in human neutrophils imaged by differentialinterference contrast (DIC) microscopy. We bifurcate the task of learning cellstatistics by leveraging GANs for the spatial component and AR models for thetemporal component. The aggregate model learned results offer a promisingcomputational environment for studying changes in organellar shape, quantity,and spatial distribution over large sequences.

Generative Spatiotemporal Modeling Of Neutrophil Behavior