  Chest X-ray (CXR) is perhaps the most frequently-performed radiological
investigation globally. In this work, we present and study several machine
learning approaches to develop automated CXR diagnostic models. In particular,
we trained several Convolutional Neural Networks (CNN) on the CheXpert dataset,
a large collection of more than 200k CXR labeled images. Then, we used the
trained CNNs to compute embeddings of the CXR images, in order to train two
sets of tree-based classifiers from them. Finally, we described and compared
three ensembling strategies to combine together the classifiers trained. Rather
than expecting some performance-wise benefits, our goal in this work is showing
that the above two methodologies, i.e., the extraction of image embeddings and
models ensembling, can be effective and viable to solve tasks that require
medical imaging understanding. Our results in that perspective are encouraging
and worthy of further investigation.
