  In standard classification, we typically treat class categories as
independent of one-another. In many problems, however, we would be neglecting
the natural relations that exist between categories, which are often dictated
by an underlying biological or physical process. In this work, we propose novel
formulations of the classification problem, based on a realization that the
assumption of class-independence is a limiting factor that leads to the
requirement of more training data. First, we propose manual ways to reduce our
data needs by reintroducing knowledge about problem-specific interclass
relations into the training process. Second, we propose a general approach to
jointly learn categorical label representations that can implicitly encode
natural interclass relations, alleviating the need for strong prior
assumptions, which are not always available. We demonstrate this in the domain
of medical images, where access to large amounts of labelled data is not
trivial. Specifically, our experiments show the advantages of this approach in
the classification of Intravenous Contrast enhancement phases in CT images,
which encapsulate multiple interesting inter-class relations.
