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.
翻译:将深度模型推广到来自新中心的新数据中(这里的域)仍是一个挑战。 这在很大程度上归因于数据统计在源和无形领域之间的变化。 最近,基于梯度的元学习方法,将培训数据分为元培训和元测试组,以模拟和处理培训期间的域转移,这些方法显示,在模拟和处理培训期间的域转移方面,效果有所改善。然而,目前完全监督的元学习方法对于医学图像分割来说是无法伸缩的,需要大力创建像素说明。与此同时,在低数据制度中,模拟域转移可能无法在源和隐蔽领域之间接近真实域的转移。为了解决这个问题,我们提出了一个新型的半监督的元学习框架。我们明确地模拟了与域转移有关的表述。将表达方式分离并结合来重建输入图像,就可以使用无标签的数据来更好地估计元学习的真实域转移。因此,模型可以实现更好的概括性表现,特别是在标签数据数量有限的情况下。 实验显示,拟议的方法在不同的分区任务和标准上,在不同的公共分级任务上是稳健的。