论文标题
使用磁共振图像扫描的临床前阶段阿尔茨海默氏病检测
Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans
论文作者
论文摘要
阿尔茨海默氏病是主要影响老年人而不成为衰老的疾病之一。最常见的症状包括交流和抽象思维的问题以及迷失方向。重要的是要在早期阶段检测阿尔茨海默氏病，以便通过药物和培训来改善认知功能。在本文中，我们提出了两个注意模型网络，用于从MRI图像中检测阿尔茨海默氏病，以帮助临床前阶段的早期检测工作。我们还将这两个注意网络模型的性能与基线模型进行了比较。最近可用的OASIS-3纵向神经影像学，临床和认知数据集用于训练，评估和比较我们的模型。这项研究的新颖性在于，当所有参数，物理评估和临床数据表明患者健康并且没有症状时，我们旨在检测阿尔茨海默氏病
Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a baseline model. Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive Dataset is used to train, evaluate and compare our models. The novelty of this research resides in the fact that we aim to detect Alzheimer's disease when all the parameters, physical assessments, and clinical data state that the patient is healthy and showing no symptoms
