论文标题
基于CNN-LSTM的多模式MRI和临床数据融合，用于预测中风患者的功能结果
CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
论文作者
论文摘要
临床结果预测在中风患者管理中起着重要作用。从机器学习的观点来看，主要挑战之一是在患者入院时处理异质数据，即多维图像数据和临床数据是标量。在本文中，提出了基于多模式卷积神经网络 - 长短期记忆（CNN-LSTM）的集合模型。对于每个MR图像模块，专用网络使用改良的Rankin量表（MRS）对临床结果进行初步预测。最终MRS得分是通过合并每个模块的初步概率来获得的，该模块专用于临床元数据，此时年龄或国家健康研究所中风量表（NIHSS）的特定类型的MR图像。实验结果表明，所提出的模型超过了基准，并提供了一种原始方法，可以在深度学习体系结构中自动编码MR图像的时空上下文。对于NIHSS的拟议模型，实现了最高的AUC（0.77）。
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS.
