  In this paper, we consider image quality assessment (IQA) as a measure of how
images are amenable with respect to a given downstream task, or task
amenability. When the task is performed using machine learning algorithms, such
as a neural-network-based task predictor for image classification or
segmentation, the performance of the task predictor provides an objective
estimate of task amenability. In this work, we use an IQA controller to predict
the task amenability which, itself being parameterised by neural networks, can
be trained simultaneously with the task predictor. We further develop a
meta-reinforcement learning framework to improve the adaptability for both IQA
controllers and task predictors, such that they can be fine-tuned efficiently
on new datasets or meta-tasks. We demonstrate the efficacy of the proposed
task-specific, adaptable IQA approach, using two clinical applications for
ultrasound-guided prostate intervention and pneumonia detection on X-ray
images.
