Stacked denoising autoencoders for the automatic recognition of microglial cells' state

Abstract

We present the first study for the automatic recognition of microglial cells' state using stacked denoising autoencoders. Microglia has a pivotal role as sentinel of neuronal diseases where its state (resting, transition or active) is indicative of what is occurring in the Central Nervous System. In this work we delve on different strategies to best learn a stacked denoising autoencoder for that purpose and show that the transition state is the most hard to recognize while an accuracy of approximately 64% is obtained with a dataset of 45 images.