  Motivation: Innovative microfluidic systems carry the promise to greatly
facilitate spatio-temporal analysis of single cells under well-defined
environmental conditions, allowing novel insights into population heterogeneity
and opening new opportunities for fundamental and applied biotechnology.
Microfluidics experiments, however, are accompanied by vast amounts of data,
such as time series of microscopic images, for which manual evaluation is
infeasible due to the sheer number of samples. While classical image processing
technologies do not lead to satisfactory results in this domain, modern deep
learning technologies such as convolutional networks can be sufficiently
versatile for diverse tasks, including automatic cell tracking and counting as
well as the extraction of critical parameters, such as growth rate. However,
for successful training, current supervised deep learning requires label
information, such as the number or positions of cells for each image in a
series; obtaining these annotations is very costly in this setting. Results: We
propose a novel Machine Learning architecture together with a specialized
training procedure, which allows us to infuse a deep neural network with
human-powered abstraction on the level of data, leading to a high-performing
regression model that requires only a very small amount of labeled data.
Specifically, we train a generative model simultaneously on natural and
synthetic data, so that it learns a shared representation, from which a target
variable, such as the cell count, can be reliably estimated.
