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15 March 2019 Variational autoencoding tissue response to microenvironment perturbation
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Abstract
This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.
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Geoffrey F. Schau, Guillaume Thibault, Mark A. Dane, Joe W. Gray, Laura M. Heiser, and Young Hwan Chang "Variational autoencoding tissue response to microenvironment perturbation", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491M (15 March 2019); doi: 10.1117/12.2512660; https://doi.org/10.1117/12.2512660
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