Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy
Abstract
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.
Cite
Text
Robitaille et al. "Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11426Markdown
[Robitaille et al. "Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/robitaille2018aaai-learning/) doi:10.1609/AAAI.V32I1.11426BibTeX
@inproceedings{robitaille2018aaai-learning,
title = {{Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy}},
author = {Robitaille, Louis-Émile and Durand, Audrey and Gardner, Marc-André and Gagné, Christian and De Koninck, Paul and Lavoie-Cardinal, Flavie},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {7805-7810},
doi = {10.1609/AAAI.V32I1.11426},
url = {https://mlanthology.org/aaai/2018/robitaille2018aaai-learning/}
}