  Semi-supervised learning (SSL) leverages both labeled and unlabeled data for
training models when the labeled data is limited and the unlabeled data is
vast. Frequently, the unlabeled data is more widely available than the labeled
data, hence this data is used to improve the level of generalization of a model
when the labeled data is scarce. However, in real-world settings unlabeled data
might depict a different distribution than the labeled dataset distribution.
This is known as distribution mismatch. Such problem generally occurs when the
source of unlabeled data is different from the labeled data. For instance, in
the medical imaging domain, when training a COVID-19 detector using chest X-ray
images, different unlabeled datasets sampled from different hospitals might be
used. In this work, we propose an automatic thresholding method to filter
out-of-distribution data in the unlabeled dataset. We use the Mahalanobis
distance between the labeled and unlabeled datasets using the feature space
built by a pre-trained Image-net Feature Extractor (FE) to score each unlabeled
observation. We test two simple automatic thresholding methods in the context
of training a COVID-19 detector using chest X-ray images. The tested methods
provide an automatic manner to define what unlabeled data to preserve when
training a semi-supervised deep learning architecture.
