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

Dealing with Distribution Mismatch in Semi-supervised Deep Learning for  Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature  Densities