  The medical image is characterized by the inter-class indistinction, high
variability, and noise, where the recognition of pixels is challenging. Unlike
previous self-attention based methods that capture context information from one
level, we reformulate the self-attention mechanism from the view of the
high-order graph and propose a novel method, namely Hierarchical Attention
Network (HANet), to address the problem of medical image segmentation.
Concretely, an HA module embedded in the HANet captures context information
from neighbors of multiple levels, where these neighbors are extracted from the
high-order graph. In the high-order graph, there will be an edge between two
nodes only if the correlation between them is high enough, which naturally
reduces the noisy attention information caused by the inter-class
indistinction. The proposed HA module is robust to the variance of input and
can be flexibly inserted into the existing convolution neural networks. We
conduct experiments on three medical image segmentation tasks including optic
disc/cup segmentation, blood vessel segmentation, and lung segmentation.
Extensive results show our method is more effective and robust than the
existing state-of-the-art methods.
