论文标题
阿尔茨海默氏病预测的图像分析：拥抱模型建筑设计的病理标志
Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design
论文作者
论文摘要
阿尔茨海默氏病（AD）与局部（例如脑组织萎缩）和全球大脑变化（脑连通性丧失）有关，可以通过高分辨率的结构磁共振成像来检测。通常，这些变化及其与AD的关系进行了独立研究。在这里，我们介绍了一种小说，高度可观的方法，同时捕获$ \ textit {local} $和$ \ textit {global} $变化患病大脑的变化。它基于一种神经网络结构，该神经网络结构将基于斑块的高分辨率3D-CNN与全球拓扑特征相结合，评估多规模脑组织连接。我们的本地全球方法达到了竞争成果，平均精度得分为0.95美元\ pm0.03 $，用于对认知正常受试者和AD患者的分类（患病率$ \ 55 \％$）。
Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution structural magnetic resonance imaging. Conventionally, these changes and their relation to AD are investigated independently. Here, we introduce a novel, highly-scalable approach that simultaneously captures $\textit{local}$ and $\textit{global}$ changes in the diseased brain. It is based on a neural network architecture that combines patch-based, high-resolution 3D-CNNs with global topological features, evaluating multi-scale brain tissue connectivity. Our local-global approach reached competitive results with an average precision score of $0.95\pm0.03$ for the classification of cognitively normal subjects and AD patients (prevalence $\approx 55\%$).
