Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
This addresses domain variability in medical imaging for segmentation tasks, but it is incremental as it extends an existing method.
The paper tackled the problem of deep learning models failing to generalize across domains in medical imaging segmentation, showing that self-ensembling improves generalization with small unlabelled data.
Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.