论文标题
通过图形卷积网络进行深度特征融合，用于颅内动脉标记
Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling
论文作者
论文摘要
颅内动脉是至关重要的血管，可为大脑提供含氧血液。颅内动脉标签为众多临床应用和疾病诊断提供了宝贵的指导和导航。在脑动脉的解剖标记中，已经进行了各种机器学习算法以自动化。但是，由于颅内动脉的复杂性和变化，任务仍然具有挑战性。这项研究研究了一个新型的图形卷积神经网络，具有深层特征融合用于脑动脉标记。我们在编码核核对编码器架构中介绍了堆叠的图形卷积，从图节点及其邻居中提取高级表示。此外，我们有效地汇总了来自不同层次结构的中间特征，以增强所提出的模型的表示能力和标记性能。我们在公共数据集上进行了广泛的实验，其中结果证明了我们的方法优于基线的优势。
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin.
