Learning-based methods suffer from limited clean annotations, especially for biomedical segmentation. For example, the noisy labels make model confused and the limited labels lead to an inadequate training, which are usually concomitant. In this paper, we propose a deep attention networks (DAN) that is more robust to noisy labels by eliminating the bad gradients caused by noisy labels, using attention modules. Especially, the strategy of multi-stage filtering is applied, because clear elimination in a certain layer is impossible. As the prior knowledge of noise distribution is usually unavailable, a two-stream network is developed to provide information from each other for attention modules to mine potential distribution of noisy gradients. The intuition is that a discussion of two students may find out mistakes taught by teacher. And we further analyse the infection processing of noisy labels and design three attention modules, according to different disturbance of noisy labels in different layers. Furthermore, a hierarchical distillation is developed to provide more reliable pseudo labels from unlabeld data, which further boosts the DAN. Combining our DAN and hierarchical distillation can significantly improve a model performance with deficient clean annotations. The experiments on both HVSMR 2016 and BRATS 2015 benchmarks demonstrate the effectiveness of our method.

本文提出了一个双流互注意网络(TSMAN)来弱化错误标签的反向传播梯度的影响，从而使神经网络在处理不干净的数据时更加稳健。使用半监督学习框架，通过交换双流结构内的多级特征来降低每个子网络中嘈杂标签的影响，并进一步优化性能。在HVSMR2016和BRATS2015基准测试中的实验表明，所提出的方法能够超越目前的全监督结果。

一种两流相互关注网络，用于带噪标签的半监督生物医学分割