@article{Robitaille_Durand_Gardner_Gagné_De Koninck_Lavoie-Cardinal_2018, title={Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/11426}, DOI={10.1609/aaai.v32i1.11426}, abstractNote={ <p> With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super-resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Robitaille, Louis-Émile and Durand, Audrey and Gardner, Marc-André and Gagné, Christian and De Koninck, Paul and Lavoie-Cardinal, Flavie}, year={2018}, month={Apr.} }