Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

Code

• github.com/AllenCellModeling/torch_integrated_cell
  OfficialIn papertorch★ 0
• github.com/AllenCellModeling/pytorch_integrated_cell
  pytorch★ 0

Abstract

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.

Reproductions