NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection

Abstract

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.

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