早期发现对于有效治疗阿尔茨海默病(AD)至关重要,筛查轻度认知障碍(MCI)是常见的做法。在已应用于磁共振成像(MRI)评估大脑结构变化的几种深度学习技术中,卷积神经网络(CNN)由于其在使用各种多层感知器的自动特征学习方面的卓越效率而受到欢迎。同时,集成学习(EL)已被证明是通过集成多个模型有利于学习系统性能的鲁棒性。这里,我们提出了一种结合CNN和EL开发的分类器集成,即,CNN-EL方法,使用MRI识别患有MCI或AD的受试者:即,(1)AD与健康认知(HC)之间的分类,(2)MCIC(MCI患者将转换为AD)和HC,和(3)MCIc和MCInc(不会转换为AD的MCI患者)。对于每个二元分类任务,大量的CNN模型进行了训练,应用一组矢状,日冕,或横向MRI切片;然后将这些CNN模型集成到一个集合中。使用分层五次交叉验证方法对集合的性能进行了10次评估。由在矢状之间的二元分类任务中分离两个类别的最可辨别的切片确定的交叉点的数量,日冕,和横向切片集,转变为标准的蒙特利尔神经研究所(MNI)空间,作为评估点所在的大脑区域对AD进行分类的能力的指标。因此,具有最多交叉点的脑区被认为是对AD早期诊断主要有贡献的脑区.结果显示准确率分别为0.84±0.05、0.79±0.04和0.62±0.06,用于将AD与HC,MCIcvs.HC,和MCICvs.MCInc,可与以前的报告和3D深度学习方法(3D-SENet)相媲美,该方法基于使用通道注意力机制的更先进且流行的挤压和激励网络模型。值得注意的是,交叉点准确地定位了颞叶内侧和边缘系统的其他几个结构,即,已知在AD早期被击中的大脑区域。更有趣的是,分类器揭示了AD和MCIC大脑中多种模式的MRI变化,涉及这些关键地区。这些结果表明,作为一种数据驱动的方法,CNN和EL相结合的方法可以定位经过训练的集成模型所指示的最可辨别的大脑区域,同时最大化集成模型的泛化能力,以成功捕获疾病过程早期的AD相关大脑变异;它还可以为理解AD中全脑MRI变化的复杂异质性提供新的见解。需要进一步的研究来检验这一发现的临床意义,倡导的CNN-EL方法帮助理解和评估个体受试者疾病状态的能力,症状负担和进展,以及提倡的CNN-EL方法在检测其他脑部疾病如精神分裂症时定位最可辨别的大脑区域的普遍性,自闭症,和严重的抑郁症,以数据驱动的方式。
Early detection is critical for effective management of Alzheimer\'s disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject\'s disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.