  Microtubule networks (MTs) are a component of a cell that may indicate the
presence of various chemical compounds and can be used to recognize properties
such as treatment resistance. Therefore, the classification of MT images is of
great relevance for cell diagnostics. Human experts find it particularly
difficult to recognize the levels of chemical compound exposure of a cell.
Improving the accuracy with automated techniques would have a significant
impact on cell therapy. In this paper we present the application of Deep
Learning to MT image classification and evaluate it on a large MT image dataset
of animal cells with three degrees of exposure to a chemical agent. The results
demonstrate that the learned deep network performs on par or better at the
corresponding cell classification task than human experts. Specifically, we
show that the task of recognizing different levels of chemical agent exposure
can be handled significantly better by the neural network than by human
experts.
