理解图卷积网络检测脑卒中的脑损伤

Ariel Iporre-Rivas, G. Scheuermann, C. Gillmann
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引用次数: 0

摘要

中风发作引起的脑损伤可导致患者残疾。因此,脑损伤的分割是神经学的一项重要任务。最近,这项任务主要是通过机器学习方法来解决的,这些方法被证明是非常成功的。其中一种方法是图形卷积网络(GCN),其中输入图像被解释为图形结构。与通常的神经网络一样,由于其黑箱性质,可解释性很难。我们提供了GCN中固有的激活的交互式可视化,这是原始数据集的映射。我们在输入图像上可视化底层图网络的激活值。我们通过将其应用于在真实数据集上训练的GCN来展示我们方法的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke
Brain lesions derived from stroke episodes can result in disabilities for a patient. Therefore, the segmentation of brain lesions is an important task in neurology. Recently this task has been mainly tackled by machine learning approaches that demonstrated to be very successful. One of these approaches is Graph Convolutional Networks (GCN), where the input image is interpreted as a graph structure. As usual for neural networks, the interpretability is hard due to their black-box nature. We provide an interactive visualization of the activation inherent in the GCN, which is map from the original dataset. We visualize the activation values of the underlying graph network on top of the input image. We show the usability of our approach by applying it to a GCN that was trained on a real-world dataset.
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