2019-05-24-lafarge19a.md

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Contributed Papers	Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning	We propose a novel variational autoencoder (VAE) framework for learning representations of cell images for the domain of image-based profiling, important for new therapeutic discovery. Previously, generative adversarial network-based (GAN) approaches were proposed to enable biologists to visualize structural variations in cells that drive differences in populations. However, while the images were realistic, they did not provide direct reconstructions from representations, and their performance in downstream analysis was poor. We address these limitations in our approach by adding an adversarial-driven similarity constraint applied to the standard VAE framework, and a progressive training procedure that allows higher quality reconstructions than standard VAE’s. The proposed models improve classification accuracy by 22% (to 90%) compared to the best reported GAN model, making it competitive with other models that have higher quality representations, but lack the ability to synthesize images. This provides researchers a new tool to match cellular phenotypes effectively, and also to gain better insight into cellular structure variations that are driving differences between populations of cells.	inproceedings	Proceedings of Machine Learning Research	lafarge19a	0	Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning	315	325	315-325	315	false	Lafarge, Maxime W. and Caicedo, Juan C. and Carpenter, Anne E. and Pluim, Josien P.W. and Singh, Shantanu and Veta, Mitko	given family Maxime W. Lafarge given family Juan C. Caicedo given family Anne E. Carpenter given family Josien P.W. Pluim given family Shantanu Singh given family Mitko Veta	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/lafarge19a/lafarge19a.pdf