  The placenta is a complex organ, playing multiple roles during fetal
development. Very little is known about the association between placental
morphological abnormalities and fetal physiology. In this work, we present an
open sourced, computationally tractable deep learning pipeline to analyse
placenta histology at the level of the cell. By utilising two deep
Convolutional Neural Network architectures and transfer learning, we can
robustly localise and classify placental cells within five classes with an
accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic
knowledge that is capable of both stratifying five distinct cell populations
and learn intraclass phenotypic variance. We envisage that the automation of
this pipeline to population scale studies of placenta histology has the
potential to improve our understanding of basic cellular placental biology and
its variations, particularly its role in predicting adverse birth outcomes.
