论文标题
行为得分被安排的脑编码器网络，用于改善使用静止状态fMRI的阿尔茨海默氏病分类
Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI
论文作者
论文摘要
准确检测痴呆症发作的能力对于疾病的治疗很重要。从临床上讲，阿尔茨海默氏病（AD）和轻度认知障碍（MCI）患者的诊断是基于对心理测试和大脑成像的综合评估，例如正电子发射断层扫描（PET）和解剖学磁共振成像（MRI）。在这项使用两个不同数据集的工作中，我们提出了一个行为得分插入的编码器网络（BSEN），该网络（BSEN）将定期辅助的心理测试信息集成到代表主题的静止状态fMRI数据的编码程序中，以进行自动分类任务。 BSEN基于3D卷积自动编码器结构，使用微型状态检查（MMSE）和临床痴呆评级（CDR）的行为得分共同优化对比损失。我们提出的使用BSEN的分类框架达到了59.44％的总体识别精度（3级分类：AD，MCI和健康对照），我们进一步提取了健康对照（HC）和AD患者之间最歧视的区域。
The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's restingstate fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from MiniMental State Examination (MMSE) and Clinical Dementia Rating (CDR). Our proposed classification framework of using BSEN achieved an overall recognition accuracy of 59.44% (3-class classification: AD, MCI and Healthy Control), and we further extracted the most discriminative regions between healthy control (HC) and AD patients.
