Learning Image Quality Assessment by Reinforcing Task Amenable Data Selection

Abstract

In this paper, we consider a type of image quality assessment (IQA) 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 images that lead to poor accuracy in the target task. In this work, we show that controller-predicted IQA can be significantly different from task-specific quality labels manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective IQA without a “clean” validation set, thereby avoiding the requirement for human labels of task amenability. Using 6712, labelled and segmented, clinical ultrasound images from 259 patients, experimental results on holdout data show that the proposed IQA achieved a mean classification accuracy of $0.94\pm 0.01$ and a mean segmentation Dice of $0.89\pm 0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm 0.01$ and $0.82\pm 0.02$ from networks without considering task amenability. This enables IQA feedback during real-time ultrasound acquisition among many other medical imaging applications.