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级结构。同一序列中不同磁共振成像（MRI）序列、覆盖头部的不同切片层以及每层切片内的不同区域，均传递着差异化的信息。本文提出一种分层注意力网络，用于检测临床医院环境下获取的磁共振成像（MRI）扫描中的异常。该网络适用于非体积数据（即高分辨率MRI切片堆叠），可通过基于检查级别的二元标签进行训练。研究表明，这种分层方法能够提升分类性能，同时通过粗粒度的切片间与切片内异常定位，或为不同切片及序列赋予重要性分数来提供可解释性，使模型适用于放射科室的自动化分诊系统。