  Clinical neuroimaging data is naturally hierarchical. Different magnetic
resonance imaging (MRI) sequences within a series, different slices covering
the head, and different regions within each slice all confer different
information. In this work we present a hierarchical attention network for
abnormality detection using MRI scans obtained in a clinical hospital setting.
The proposed network is suitable for non-volumetric data (i.e. stacks of
high-resolution MRI slices), and can be trained from binary examination-level
labels. We show that this hierarchical approach leads to improved
classification, while providing interpretability through either coarse inter-
and intra-slice abnormality localisation, or giving importance scores for
different slices and sequences, making our model suitable for use as an
automated triaging system in radiology departments.
