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Exposure to intense illumination light is an unavoidable consequence offluorescence microscopy, and poses a risk to the health of the sample in everylive-cell fluorescence microscopy experiment. Furthermore, the possibleside-effects of phototoxicity on the scientific conclusions that are drawn froman imaging experiment are often unaccounted for. Previously, controlling forphototoxicity in imaging experiments required additional labels andexperiments, limiting its widespread application. Here we provide aproof-of-principle demonstration that the phototoxic effects of an imagingexperiment can be identified directly from a single phase-contrast image usingdeep convolutional neural networks (ConvNets). This lays the groundwork for anautomated tool for assessing cell health in a wide range of imagingexperiments. Interpretability of such a method is crucial for its adoption. Wetake steps towards interpreting the classification mechanism of the trainedConvNet by visualizing salient features of images that contribute to accurateclassification.

DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets