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.

翻译：深度神经网络（DNNs）已广泛应用于医学图像分类，并取得了显著的分类性能。但这些成果高度依赖于大规模、精确标注的训练数据。然而，在医学图像标注过程中，由于标注工作严重依赖标注人员的专业知识和经验，标签噪声不可避免地会引入。同时，DNNs容易过拟合噪声标签，导致模型性能下降。因此，本文创新性地设计了一种抗噪声训练方法，以减轻噪声标签在医学图像分类中的负面影响。具体而言，我们将对比学习和组内注意力混合策略融入传统的监督学习中。用于特征提取器的对比学习有助于增强DNNs的视觉表征能力。组内注意力混合模块构建样本组并为组内样本分配自注意力权重，随后通过加权混合操作生成大量抑制噪声的样本。我们在多种噪声水平下的合成和真实噪声医学数据集上进行了对比实验。严格的实验验证表明，我们结合对比学习和注意力混合的抗噪声方法能够有效处理标签噪声，并优于现有最优方法。消融研究也表明，两个组件均有助于提升模型性能。所提方法展示了其抑制标签噪声的能力，并在实际临床应用方面具有潜在价值。