  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.
