  Harmonizing the analysis of data, especially of 3-D image volumes, consisting
of different number of slices and annotated per volume, is a significant
problem in training and using deep neural networks in various applications,
including medical imaging. Moreover, unifying the decision making of the
networks over different input datasets is crucial for the generation of rich
data-driven knowledge and for trusted usage in the applications. This paper
presents a new deep neural architecture, named RACNet, which includes routing
and feature alignment steps and effectively handles different input lengths and
single annotations of the 3-D image inputs, whilst providing highly accurate
decisions. In addition, through latent variable extraction from the trained
RACNet, a set of anchors are generated providing further insight on the
network's decision making. These can be used to enrich and unify data-driven
knowledge extracted from different datasets. An extensive experimental study
illustrates the above developments, focusing on COVID-19 diagnosis through
analysis of 3-D chest CT scans from databases generated in different countries
and medical centers.
