Abstract
Semantic segmentation plays an important role in biomedical image analysis applications. Convolutional neural networks (CNN) have become a promising approach to segment biomedical images. Nevertheless, the accuracy of these methods is highly dependent on the training data. These models may not generalize well to unseen image domains due to the phenomenon of domain shift. We propose a self-ensembling attention networks to address domain shift for biomedical image segmentation. There are two main components in the proposed network: a student network which plays a role of the base networks and a teacher network which plays a role of the ensemble networks. As the iteration goes on, the student network becomes more accurate, and the ensemble predictions in the teacher network also get closer to the correct labels in the target domain. In this way, domain-invariant features can be learnt correspondingly. We use the DRIVE, STARE, HRF and CHASEDB eye vasculature segmentation datasets and show that our approach can significantly improve results where we only use labels of one domain in training and test on the other domain.