  In the context of the global coronavirus pandemic, different deep learning
solutions for infected subject detection using chest X-ray images have been
proposed. However, deep learning models usually need large labelled datasets to
be effective. Semi-supervised deep learning is an attractive alternative, where
unlabelled data is leveraged to improve the overall model's accuracy. However,
in real-world usage settings, an unlabelled dataset might present a different
distribution than the labelled dataset (i.e. the labelled dataset was sampled
from a target clinic and the unlabelled dataset from a source clinic). This
results in a distribution mismatch between the unlabelled and labelled
datasets. In this work, we assess the impact of the distribution mismatch
between the labelled and the unlabelled datasets, for a semi-supervised model
trained with chest X-ray images, for COVID-19 detection. Under strong
distribution mismatch conditions, we found an accuracy hit of almost 30\%,
suggesting that the unlabelled dataset distribution has a strong influence in
the behaviour of the model. Therefore, we propose a straightforward approach to
diminish the impact of such distribution mismatch. Our proposed method uses a
density approximation of the feature space. It is built upon the target dataset
to filter out the observations in the source unlabelled dataset that might harm
the accuracy of the semi-supervised model. It assumes that a small labelled
source dataset is available together with a larger source unlabelled dataset.
Our proposed method does not require any model training, it is simple and
computationally cheap. We compare our proposed method against two popular state
of the art out-of-distribution data detectors, which are also cheap and simple
to implement. In our tests, our method yielded accuracy gains of up to 32\%,
when compared to the previous state of the art methods.
