Reading notes: Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
This blog is the reading note for the paper "Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks." by He, Xiang, et al., AAAI 2019. Broadly speaking, this paper deals with the problem of adversarial attacks on biomedical image segmentation model. Specifically, it proposes a non-local context encoder which can model short- and long-range spatial dependencies and encode global contexts to enhance the adversarial robustness. Introduction Medical image segmentation is a fundamental part of image analysis for computer-aided diagnosis, which offers pixel-level annotation for ROI such as organs, lesion, structures on the medical image (e.g. MRI, CT, X-ray). However, it is challenging due to the limited number and diversity of training dataset. With the development of the hardware, CNN-based methods such as UNet, FCN have achieved great success in medical segmentation. In parallel to this progress, recent work shows that semantic segment