  Predicting if red blood cells (RBC) are infected with the malaria parasite is
an important problem in Pathology. Recently, supervised machine learning
approaches have been used for this problem, and they have had reasonable
success. In particular, state-of-the-art methods such as Convolutional Neural
Networks automatically extract increasingly complex feature hierarchies from
the image pixels. While such generalized automatic feature extraction methods
have significantly reduced the burden of feature engineering in many domains,
for niche tasks such as the one we consider in this paper, they result in two
major problems. First, they use a very large number of features (that may or
may not be relevant) and therefore training such models is computationally
expensive. Further, more importantly, the large feature-space makes it very
hard to interpret which features are truly important for predictions. Thus, a
criticism of such methods is that learning algorithms pose opaque black boxes
to its users, in this case, medical experts. The recommendation of such
algorithms can be understood easily, but the reason for their recommendation is
not clear. This is the problem of non-interpretability of the model, and the
best-performing algorithms are usually the least interpretable. To address
these issues, in this paper, we propose an approach to extract a very small
number of aggregated features that are easy to interpret and compute, and
empirically show that we obtain high prediction accuracy even with a
significantly reduced feature-space.
