  Generalising deep models to new data from new centres (termed here domains)
remains a challenge. This is largely attributed to shifts in data statistics
(domain shifts) between source and unseen domains. Recently, gradient-based
meta-learning approaches where the training data are split into meta-train and
meta-test sets to simulate and handle the domain shifts during training have
shown improved generalisation performance. However, the current fully
supervised meta-learning approaches are not scalable for medical image
segmentation, where large effort is required to create pixel-wise annotations.
Meanwhile, in a low data regime, the simulated domain shifts may not
approximate the true domain shifts well across source and unseen domains. To
address this problem, we propose a novel semi-supervised meta-learning
framework with disentanglement. We explicitly model the representations related
to domain shifts. Disentangling the representations and combining them to
reconstruct the input image allows unlabeled data to be used to better
approximate the true domain shifts for meta-learning. Hence, the model can
achieve better generalisation performance, especially when there is a limited
amount of labeled data. Experiments show that the proposed method is robust on
different segmentation tasks and achieves state-of-the-art generalisation
performance on two public benchmarks.
