# Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

arXiv: https://arxiv.org/abs/1705.00092 · HTML: https://mlsanity.com/paper/1705.00092/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2017.

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

- [Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy](https://mlsanity.com/paper/2106.08285/) (2021)
- [Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants](https://mlsanity.com/paper/2302.02896/) (2023)

## Abstract (arXiv)

We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.