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Variational autoencoding tissue response to microenvironment perturbation
Author(s): Geoffrey F. Schau; Guillaume Thibault; Mark A. Dane; Joe W. Gray; Laura M. Heiser; Young Hwan Chang
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Paper 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.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491M (15 March 2019); doi: 10.1117/12.2512660
Show Author Affiliations
Geoffrey F. Schau, Oregon Health & Science Univ. (United States)
Guillaume Thibault, Oregon Health & Science Univ. (United States)
Mark A. Dane, Oregon Health & Science Univ. (United States)
Joe W. Gray, Oregon Health & Science Univ. (United States)
Laura M. Heiser, Oregon Health & Science Univ. (United States)
Young Hwan Chang, Oregon Health & Science Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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