  Recent progress in biomedical image segmentation based on deep convolutional
neural networks (CNNs) has drawn much attention. However, its vulnerability
towards adversarial samples cannot be overlooked. This paper is the first one
that discovers that all the CNN-based state-of-the-art biomedical image
segmentation models are sensitive to adversarial perturbations. This limits the
deployment of these methods in safety-critical biomedical fields. In this
paper, we discover that global spatial dependencies and global contextual
information in a biomedical image can be exploited to defend against
adversarial attacks. To this end, non-local context encoder (NLCE) is proposed
to model short- and long range spatial dependencies and encode global contexts
for strengthening feature activations by channel-wise attention. The NLCE
modules enhance the robustness and accuracy of the non-local context encoding
network (NLCEN), which learns robust enhanced pyramid feature representations
with NLCE modules, and then integrates the information across different levels.
Experiments on both lung and skin lesion segmentation datasets have
demonstrated that NLCEN outperforms any other state-of-the-art biomedical image
segmentation methods against adversarial attacks. In addition, NLCE modules can
be applied to improve the robustness of other CNN-based biomedical image
segmentation methods.
