  In many practical applications, deep neural networks have been typically
deployed to operate as a black box predictor. Despite the high amount of work
on interpretability and high demand on the reliability of these systems, they
typically still have to include a human actor in the loop, to validate the
decisions and handle unpredictable failures and unexpected corner cases. This
is true in particular for failure-critical application domains, such as medical
diagnosis. We present a novel approach to explain and support an interpretation
of the decision-making process to a human expert operating a deep learning
system based on Convolutional Neural Network (CNN). By modeling activation
statistics on selected layers of a trained CNN via Gaussian Mixture Models
(GMM), we develop a novel perceptual code in binary vector space that describes
how the input sample is processed by the CNN. By measuring distances between
pairs of samples in this perceptual encoding space, for any new input sample,
we can now retrieve a set of most perceptually similar and dissimilar samples
from an existing atlas of labeled samples, to support and clarify the decision
made by the CNN model. Possible uses of this approach include for example
Computer-Aided Diagnosis (CAD) systems working with medical imaging data, such
as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We
demonstrate the viability of our method in the domain of medical imaging for
patient condition diagnosis, as the proposed decision explanation method via
similar ground truth domain examples (e.g. from existing diagnosis archives)
will be interpretable by the operating medical personnel. Our results indicate
that our method is capable of detecting distinct prediction strategies that
enable us to identify the most similar predictions from an existing atlas.
