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.

在临床医院环境中，使用磁共振成像（MRI）扫描，我们提出了一种基于分层注意力网络的异常检测方法。这种分层方法提高了分类精度，同时通过粗糙的切片异常定位或不同切片和序列的重要性评分来解释模型的结果，使其适用于放射科部门的自动分诊系统。