Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.

翻译：医学图像在临床实践中常受到图像退化的影响，导致基于深度学习的模型性能下降。为解决这一问题，以往的研究主要侧重于过滤导致退化的低质量图像，而忽略了其对模型的潜在价值。通过有效学习并利用退化知识，模型能够更好地抵御其不利影响，从而避免误诊。在本文中，我们提出了图像质量感知诊断问题，旨在利用低质量图像及其质量标签实现更准确、鲁棒的诊断。然而，退化的多样性以及图像质量评估与疾病诊断之间表面无关的目标，使得有效利用质量标签辅助诊断仍颇具挑战。为此，我们提出了一种新颖的元知识联合嵌入网络，由两个子网组成：任务网络和元学习器。任务网络通过知识联合嵌入特征构建显式的质量信息利用机制以增强诊断，而元学习器则通过元学习和联合编码掩码确保这些特征的有效性并约束其语义。在四个常用医学成像模态的五个数据集上的优异性能证明了我们方法的有效性和泛化能力。