Label-noise-tolerant medical image classification via self-attention and
  self-supervised learning
Hongyang Jiang Mengdi Gao Yan Hu Qiushi Ren Zhaoheng Xie Jiang Liu
Deep neural networks (DNNs) have been widely applied in medical image
classification and achieve remarkable classification performance. These
achievements heavily depend on large-scale accurately annotated training data.
However, label noise is inevitably introduced in the medical image annotation,
as the labeling process heavily relies on the expertise and experience of
annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the
performance of models. Therefore, in this work, we innovatively devise
noise-robust training approach to mitigate the adverse effects of noisy labels
in medical image classification. Specifically, we incorporate contrastive
learning and intra-group attention mixup strategies into the vanilla supervised
learning. The contrastive learning for feature extractor helps to enhance
visual representation of DNNs. The intra-group attention mixup module
constructs groups and assigns self-attention weights for group-wise samples,
and subsequently interpolates massive noisy-suppressed samples through weighted
mixup operation. We conduct comparative experiments on both synthetic and
real-world noisy medical datasets under various noise levels. Rigorous
experiments validate that our noise-robust method with contrastive learning and
attention mixup can effectively handle with label noise, and is superior to
state-of-the-art methods. An ablation study also shows that both components
contribute to boost model performance. The proposed method demonstrates its
capability of curb label noise and has certain potential toward real-world
clinic applications.