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.

翻译：临床神经影像数据天然具有层次结构。同一序列中不同磁共振成像序列、覆盖头部的不同切片以及每个切片内的不同区域均传递不同信息。本研究提出一种层次化注意力网络，用于检测临床医院环境中获得的MRI扫描异常。该网络适用于非体积数据（即高分辨率MRI切片堆叠），且可通过二元检查级别标签进行训练。研究表明，这种层次化方法能在提升分类性能的同时，通过提供粗粒度的切片间/切片内异常定位，或为不同切片与序列赋予重要性分数来实现可解释性，使模型适用于放射科自动化分诊系统。