Abstract
Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide. The selection of patients for the most optimal ischaemic stroke treatment is a crucial step for a successful outcome, as the effect of treatment highly depends on the time to treatment. We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information, acquired on hospital admission, to predict the functional outcome of stroke treatment based on the modified Rankin Scale (mRS). This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUC score of 0.85.
Abstract (translated)
急性缺血性中风是由大脑组织Blood flow 中断引起的,是世界上残疾和死亡的主要原因。选择最佳的缺血性中风治疗是实现成功结果的关键步骤,因为治疗的效果高度取决于治疗的时间来。我们提出了一种基于Transformer的多模式网络(TranSOP),以分类方法,该方法使用患者在住院期间收集的临床 metadata 和影像学信息,根据修订的甘特图谱(mRS)预测中风治疗的功能结果。该方法包括一个融合模块,以高效结合3D非对比CT(NCCT)特征和临床信息。在利用MR Clean数据集的 unimodal 和多模式数据进行比较实验中,我们实现了最先进的AUC得分为0.85。
URL
https://arxiv.org/abs/2301.10829
