  Convolutional neural networks trained on publicly available medical imaging
datasets (source domain) rarely generalise to different scanners or acquisition
protocols (target domain). This motivates the active field of domain
adaptation. While some approaches to the problem require labeled data from the
target domain, others adopt an unsupervised approach to domain adaptation
(UDA). Evaluating UDA methods consists of measuring the model's ability to
generalise to unseen data in the target domain. In this work, we argue that
this is not as useful as adapting to the test set directly. We therefore
propose an evaluation framework where we perform test-time UDA on each subject
separately. We show that models adapted to a specific target subject from the
target domain outperform a domain adaptation method which has seen more data of
the target domain but not this specific target subject. This result supports
the thesis that unsupervised domain adaptation should be used at test-time,
even if only using a single target-domain subject
