---
# System prepended metadata

title: Supervised learning to approximate model observer in task-based measure of image quality
tags: [VAADER, Reading Group]

---

# Supervised learning to approximate model observer in task-based measure of image quality
_by Meriem Bayou-Outtas (IETR-VAADER) - 2021.06.16_ 
###### tags: `VAADER` `Reading Group`



## Abstract

<div style="text-align: justify"> 
The ability of an observer to perform a specific task on images, produced by a given medical imaging systems, defines an objective measure of image quality.

If the observer is “numerical”, can deep learning methods “do the job”? What we found in the literature? Some papers rise this issue and propose to approximate the Ideal Observer for performing tasks detection and localization. 
</div>

## Related Material
[1] [Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods, IEEE TMI 2019](https://arxiv.org/pdf/1905.06330.pdf)
[2] [Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods, IEEE TMI 2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9139307)
[3] [Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks, IEEE TMI  2021](https://arxiv.org/pdf/2104.14037.pdf)

## Slides

{%pdf https://florianlemarchand.github.io/ressources/pdfs/VAADER_Reading_Group/2021-16-06-Outtas-approximate_observer.pdf %}

## Presentation and Discussions
<iframe src="https://videos.insa-rennes.fr/video/0298-vaader-reading-group-9-meriem-outtas-supervised-learning-to-approximate-model-observer-in-task-based-measure-of-image-quality/?is_iframe=true" width="640" height="360" style="padding: 0; margin: 0; border:0" allowfullscreen ></iframe>



