  Exposure to intense illumination light is an unavoidable consequence of
fluorescence microscopy, and poses a risk to the health of the sample in every
live-cell fluorescence microscopy experiment. Furthermore, the possible
side-effects of phototoxicity on the scientific conclusions that are drawn from
an imaging experiment are often unaccounted for. Previously, controlling for
phototoxicity in imaging experiments required additional labels and
experiments, limiting its widespread application. Here we provide a
proof-of-principle demonstration that the phototoxic effects of an imaging
experiment can be identified directly from a single phase-contrast image using
deep convolutional neural networks (ConvNets). This lays the groundwork for an
automated tool for assessing cell health in a wide range of imaging
experiments. Interpretability of such a method is crucial for its adoption. We
take steps towards interpreting the classification mechanism of the trained
ConvNet by visualizing salient features of images that contribute to accurate
classification.
