Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy

Autor:	Robitaille, Louis-Émile, Durand, Audrey, Gardner, Marc-André, Gagné, Christian, De Koninck, Paul, Lavoie-Cardinal, Flavie
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the Thirtieth Innovative Applications of Artificial Intelligence Conference (IAAI), 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1803.10806 Zobrazit plný text záznamu View this record from Arxiv