# Towards Deep Cellular Phenotyping in Placental Histology

arXiv: https://arxiv.org/abs/1804.03270 · HTML: https://mlsanity.com/paper/1804.03270/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Resolution enhancement of placenta histological images using deep learning](https://mlsanity.com/paper/2208.00163/) (2022)
- [Automated segmentation and morphological characterization of placental histology images based on a single labeled image](https://mlsanity.com/paper/2210.03566/) (2022)

## Abstract (arXiv)

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.