@proceedings {20156,
	title = {Deep-learning-assisted visualization for live-cell images},
	year = {2017},
	month = {09/2017},
	publisher = {IEEE},
	address = {Beijing, China},
	abstract = {Analyzing live-cell images is particularly challenging because cells move at the same time they undergo systematic changes. Visually inspecting live-cell images therefore involves simultaneously tracking individual cells and detecting relevant spatio-temporal changes. The high cognitive burden of such a complex task makes this kind of analysis inefficient and error-prone. In this paper we describe a deep-learning-assisted visualization based on automatically derived high-level features to identify target cell changes in live-cell images. Applying a novel user-mediated color assignment scheme that maps abstract features into corresponding colors, we create color-based visual annotations that facilitate visual reasoning and analysis of complex time varying
live-cell imagery datasets. The visual representations
can be used to study temporal changes in cells, such as the morphological changes in cell at various stages of life cycle.},
	keywords = {deep learning, live-cell images, Visualization},
	author = {Hsueh-Chien Cheng and Cardone, Antonio and Krokos, Eric and Stoica, Bogdan and Faden, Alan and Varshney, Amitabh}
}
