Anti-adversarial Consistency Regularization for Data Augmentation: Applications to Robust Medical Image Segmentation

Modern deep learning methods for semantic segmentation require labor-intensive labeling for large-scale datasets with dense pixel-level annotations. Recent data augmentation methods such as dropping, mixing image patches, and adding random noises suggest effective ways to address the labeling issues for natural images. However, they can only be restrictively applied to medical image segmentation as they carry risks of distorting or ignoring the underlying clinical information of local regions of interest in an image. In this paper, we propose a novel data augmentation method for medical image segmentation without losing the semantics of the key objects (e.g., polyps). This is achieved by perturbing the objects with quasi-imperceptible adversarial noises and training a network to expand discriminative regions with a guide of anti-adversarial noises. Such guidance can be realized by a consistency regularization between the two contrasting data, and the strength of regularization is automatically and adaptively controlled considering their prediction uncertainty. Our proposed method significantly outperforms various existing methods with high sensitivity and Dice scores and extensive experiment results with multiple backbones on two datasets validate its effectiveness.