Rating Super-Resolution Microscopy Images with Deep Learning
Abstract
With super-resolution optical microscopy, it is now possible to observe molecular mechanisms. The quality of the obtained images vary a lot depending on the samples and the imaging parameters. Moreover, evaluating this quality is a difficult task. In this work, we want to learn the quality function from scores provided by experts. We propose the use of a deep network that output a quality score for a given image. A user study evaluate the quality of the predictions against human expert scores.
Cite
Text
Robitaille et al. "Rating Super-Resolution Microscopy Images with Deep Learning." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12186Markdown
[Robitaille et al. "Rating Super-Resolution Microscopy Images with Deep Learning." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/robitaille2018aaai-rating/) doi:10.1609/AAAI.V32I1.12186BibTeX
@inproceedings{robitaille2018aaai-rating,
title = {{Rating Super-Resolution Microscopy Images with Deep Learning}},
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 = {8141-8142},
doi = {10.1609/AAAI.V32I1.12186},
url = {https://mlanthology.org/aaai/2018/robitaille2018aaai-rating/}
}