# Learning image quality assessment by reinforcing task amenable data selection

arXiv: https://arxiv.org/abs/2102.07615 · HTML: https://mlsanity.com/paper/2102.07615/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [AM-LFS: AutoML for Loss Function Search](https://mlsanity.com/paper/1905.07375/) (2019)
- [Comparing Different Deep Learning Architectures for Classification of Chest Radiographs](https://mlsanity.com/paper/2002.08991/) (2020)
- [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2006.05847/) (2020)
- [Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound](https://mlsanity.com/paper/2008.03435/) (2020)
- [Image quality assessment for closed-loop computer-assisted lung ultrasound](https://mlsanity.com/paper/2008.08840/) (2020)

## Built on by (most cited first, 5 of 5)

- [Image quality assessment for machine learning tasks using meta-reinforcement learning](https://mlsanity.com/paper/2203.14258/) (2022)
- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [Adaptable image quality assessment using meta-reinforcement learning of task amenability](https://mlsanity.com/paper/2108.04359/) (2021)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [Competing for pixels: a self-play algorithm for weakly-supervised segmentation](https://mlsanity.com/paper/2405.16628/) (2024)

## Abstract (arXiv)

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using 6712, labelled and segmented, clinical ultrasound images from 259 patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of 0.94±0.01 and a mean segmentation Dice of 0.89±0.02, by discarding 5% and 15% of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective 0.90±0.01 and 0.82±0.02 from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications.