Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

By Rory M. Donovan-Maiye, Mary M. Maleckar. 2017

37 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

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Citations in the corpus

Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction (2018, background): “Recently, much effort has been invested in building generative models of cells 90 using adversarial generative models 91–93 .”
CytoGAN: Generative Modeling of Cell Images (2017, methodology): “[8], wherein GANs were used to model cell images, although their applications did not include morphological profiling.”
Synthetic Data for Deep Learning (2019, background): “[286] undertake an ambitious project: they learn a generative model of the variation in cell and nuclear morphology based on fluorescence microscopy images.”
Building a 3D Integrated Cell (2017, methodology): “Since the post of our initial preprint manuscript and software (Johnson et al., 2017), there has been increased interest in the application of GANs to modeling cell morphology and organization.”