Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, and Department of Medical Physics & Biomedical Engineering, University College London, London, UK.
Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, and Department of Medical Physics & Biomedical Engineering, University College London, London, UK; InstaDeep, London, UK.
Med Image Anal. 2022 May;78:102427. doi: 10.1016/j.media.2022.102427. Epub 2022 Mar 21.
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.
在本文中，我们将图像质量评估（IQA）视为衡量图像对于给定下游任务的可接受程度的一种方法，即任务可接受性。当任务使用机器学习算法（例如基于神经网络的图像分类或分割任务预测器）执行时，任务预测器的性能提供了任务可接受性的客观估计。在这项工作中，我们使用 IQA 控制器来预测任务可接受性，该控制器本身由神经网络参数化，可以与任务预测器同时训练。我们进一步开发了一个元强化学习框架来提高 IQA 控制器和任务预测器的适应性，以便它们可以在新数据集或元任务上进行有效的微调。我们使用两个临床应用程序（用于超声引导前列腺介入和 X 射线图像上的肺炎检测）来演示所提出的特定于任务、可适应的 IQA 方法的有效性。
