论文标题
基于图像功能使用合奏学习的MRI硬件故障预测
Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning
论文作者
论文摘要
为了确保无故障操作，预测硬件故障至关重要。这特别适用于医疗系统。我们的目标是确定在失败之前需要交换的硬件。在这项工作中，我们专注于使用与图像相关的测量值来预测20通道头部/颈部线圈的故障。因此，我们旨在解决两个类别的分类问题，即正常和断裂的线圈。为了解决这个问题，我们使用两个不同级别的数据。一个级别是指每个单个线圈通道的一维功能，我们在其上找到了完全连接的神经网络以表现最佳。其他数据级别使用代表整体线圈条件的矩阵并为不同的神经网络提供。我们堆叠了这两个网络的预测，并培训一个随机的森林分类器作为合奏学习者。因此，结合两种训练模型的见解可改善预测结果，并使我们能够以94.14％的F评分确定线圈状况，精度为99.09％。
In order to ensure trouble-free operation, prediction of hardware failures is essential. This applies especially to medical systems. Our goal is to determine hardware which needs to be exchanged before failing. In this work, we focus on predicting failures of 20-channel Head/Neck coils using image-related measurements. Thus, we aim to solve a classification problem with two classes, normal and broken coil. To solve this problem, we use data of two different levels. One level refers to one-dimensional features per individual coil channel on which we found a fully connected neural network to perform best. The other data level uses matrices which represent the overall coil condition and feeds a different neural network. We stack the predictions of those two networks and train a Random Forest classifier as the ensemble learner. Thus, combining insights of both trained models improves the prediction results and allows us to determine the coil's condition with an F-score of 94.14% and an accuracy of 99.09%.
