  The paper presents a novel deep learning approach, which extracts latent
information from trained Deep Neural Networks (DNNs) and derives concise
representations that are analyzed in an effective, unified way for prediction
purposes. It is well known that DNNs are capable of analyzing complex data;
however, they lack transparency in their decision making, in the sense that it
is not straightforward to justify their prediction, or to visualize the
features on which the decision was based. Moreover, they generally require
large amounts of data in order to learn and become able to adapt to different
environments. This makes their use difficult in healthcare, where trust and
personalization are key issues. Transparency combined with high prediction
accuracy are the targeted goals of the proposed approach. It includes both
supervised DNN training and unsupervised learning of latent variables extracted
from the trained DNNs. Domain Adaptation from multiple sources is also
presented as an extension, where the extracted latent variable representations
are used to generate predictions in other, non-annotated, environments.
Successful application is illustrated through a large experimental study in
various fields: prediction of Parkinson's disease from MRI and DaTScans;
prediction of COVID-19 and pneumonia from CT scans and X-rays; optical
character verification in retail food packaging.
