Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
翻译:阿尔茨海默氏老年痴呆症是导致痴呆症的最常见原因。 准确诊断和预测这一疾病对于设计适当的治疗计划至关重要, 提高患者的预期寿命。 已经从神经成像数据, 如结构磁共振成像等神经成像数据中, 对使用机器学习来识别阿尔茨海默氏病进行了大量研究研究。 近年来, 计算机视野的深层次学习进展表明这一问题有了新的研究方向。 然而, 目前这个领域的深层次基于学习的方法有一些缺陷, 包括模型决定的可解释性、 缺乏可概括性信息, 与传统机器学习技术相比性能较低。 在本文中, 我们设计了克服这些限制的两阶段框架。 在第一阶段, 使用125 U- Net 的组合来评分输入图像, 产生反映福克斯一级疾病严重程度的3D地图。 这个地图有助于将疾病引起的不正常的大脑区域本地化。 在第二阶段, 我们用生成的制成的制成图和其他关于该主题的信息来模拟。 我们提议使用一个两阶段的比较性能分析框架, 我们用一个更精确的模型分析框架 来展示一个不同的分析结果。 我们的模型的模型分析系统, 展示一个不同的分析框架。