  \begin{abstract} Learning-based methods suffer from a deficiency of clean
annotations, especially in biomedical segmentation. Although many
semi-supervised methods have been proposed to provide extra training data,
automatically generated labels are usually too noisy to retrain models
effectively. In this paper, we propose a Two-Stream Mutual Attention Network
(TSMAN) that weakens the influence of back-propagated gradients caused by
incorrect labels, thereby rendering the network robust to unclean data. The
proposed TSMAN consists of two sub-networks that are connected by three types
of attention models in different layers. The target of each attention model is
to indicate potentially incorrect gradients in a certain layer for both
sub-networks by analyzing their inferred features using the same input. In
order to achieve this purpose, the attention models are designed based on the
propagation analysis of noisy gradients at different layers. This allows the
attention models to effectively discover incorrect labels and weaken their
influence during the parameter updating process. By exchanging multi-level
features within the two-stream architecture, the effects of noisy labels in
each sub-network are reduced by decreasing the updating gradients. Furthermore,
a hierarchical distillation is developed to provide more reliable pseudo labels
for unlabelded data, which further boosts the performance of our retrained
TSMAN. The experiments using both the HVSMR 2016 and BRATS 2015 benchmarks
demonstrate that our semi-supervised learning framework surpasses the
state-of-the-art fully-supervised results.
