论文标题
使用多模式数据预测血栓切除术功能结果
Prediction of Thrombectomy Functional Outcomes using Multimodal Data
论文作者
论文摘要
最近的随机临床试验表明，缺血性中风的患者（由于大量颅内血管的遮挡}受益于血管内血栓切除术。但是，预测个别患者的治疗结果仍然是一个挑战。我们提出了一种新型的深度学习方法，以直接利用从图像中提取的多模式数据（临床元数据信息，成像数据和成像生物标志物）来估算血管内治疗的成功。我们将注意力机制纳入架构中，以在频道和空间上对全局特征相互依存的建模进行建模。我们使用单峰和多模式数据进行比较实验，以预测功能结果（修改后的Rankin量表得分，MRS），并为二分的MRS分数实现0.75 AUC，单个MRS得分的分类精度为0.35。
Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.
