  In the absence of sufficient data variation (e.g., scanner and protocol
variability) in annotated data, deep neural networks (DNNs) tend to overfit
during training. As a result, their performance is significantly lower on data
from unseen sources compared to the performance on data from the same source as
the training data. Semi-supervised domain adaptation methods can alleviate this
problem by tuning networks to new target domains without the need for annotated
data from these domains. Adversarial domain adaptation (ADA) methods are a
popular choice that aim to train networks in such a way that the features
generated are domain agnostic. However, these methods require careful
dataset-specific selection of hyperparameters such as the complexity of the
discriminator in order to achieve a reasonable performance. We propose to use
knowledge distillation (KD) -- an efficient way of transferring knowledge
between different DNNs -- for semi-supervised domain adaption of DNNs. It does
not require dataset-specific hyperparameter tuning, making it generally
applicable. The proposed method is compared to ADA for segmentation of white
matter hyperintensities (WMH) in magnetic resonance imaging (MRI) scans
generated by scanners that are not a part of the training set. Compared with
both the baseline DNN (trained on source domain only and without any adaption
to target domain) and with using ADA for semi-supervised domain adaptation, the
proposed method achieves significantly higher WMH dice scores.
