基于临床MRI扫描多模态融合的阿尔茨海默病自动检测
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
AI总结：
本文提出一种基于AlexNet的多模态深度学习模型，融合T1和FLAIR脑MRI扫描，用于自动区分CN、MCI和AD三个疾病阶段。
摘要：
美国人口老龄化正在推动阿尔茨海默病的患病率上升。Brookmeyer等人预测，到2060年，大约有1500万美国人将患有临床阿尔茨海默病或轻度认知障碍。为回应这一紧迫需求，人们已开发出用于预防和早期干预的阿尔茨海默病早期检测方法。值得注意的是，关于深度学习在该疾病自动检测中应用的文献正在迅速增加。本研究在以往文献的基础上，继续关注利用多模态信息来提升自动检测能力。我们旨在基于两种不同类型的脑MRI扫描，预测疾病阶段——认知正常（CN）、轻度认知障碍（MCI）和阿尔茨海默病（AD）。我们设计了一个基于AlexNet的深度学习模型，学习来自T1和FLAIR MRI扫描的互补信息的协同作用。
英文摘要：
The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
