  Due to the complexity of medical image acquisition and the difficulty of
annotation, medical image datasets inevitably contain noise. Noisy data with
wrong labels affects the robustness and generalization ability of deep neural
networks. Previous noise learning methods mainly considered noise arising from
images being mislabeled, i.e. label noise, assuming that all mislabeled images
are of high image quality. However, medical images are prone to suffering
extreme quality issues, i.e. data noise, where discriminative visual features
are missing for disease diagnosis. In this paper, we propose a noise learning
framework, termed as QMix, that learns a robust disease diagnosis model under
mixed noise. QMix alternates between sample separation and quality-aware
semisupervised training in each training epoch. In the sample separation phase,
we design a joint uncertainty-loss criterion to effectively separate (1)
correctly labeled images; (2) mislabeled images with high quality and (3)
mislabeled images with low quality. In the semi-supervised training phase, we
train a disease diagnosis model to learn robust feature representation from the
separated samples. Specifically, we devise a sample-reweighing loss to mitigate
the effect of mislabeled images with low quality during training. Meanwhile, a
contrastive enhancement loss is proposed to further distinguish mislabeled
images with low quality from correctly labeled images. QMix achieved
state-of-the-art disease diagnosis performance on five public retinal image
datasets and exhibited substantial improvement on robustness against mixed
noise.
