Ortiz Andrés, Munilla Jorge, Górriz Juan M, Ramírez Javier
1 Communications Engineering Department, University of Málaga, 29071/Málaga, Spain.
2 Department of Signal Theory, Communications and Networking, University of Granada, 18060/Granada, Spain.
Int J Neural Syst. 2016 Nov;26(7):1650025. doi: 10.1142/S0129065716500258. Epub 2016 Apr 4.
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer's disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.
计算机辅助诊断（CAD）是早期诊断阿尔茨海默病（AD）的一项重要工具，这反过来又使得能够应用更简单且更有可能有效的治疗方法。本文探讨了基于深度学习架构构建分类方法，这些架构应用于由自动解剖标记（AAL）定义的脑区。根据AAL图谱定义的区域，将每个脑区的灰质（GM）图像分割成3D小块，并使用这些小块来训练不同的深度信念网络。然后组成一个深度信念网络集成，最终预测由投票方案确定。实现并比较了两种基于深度学习的结构和四种不同的投票方案，结果得到了一种强大的分类架构，其中判别特征是以无监督方式计算的。使用来自阿尔茨海默病神经影像倡议（ADNI）的大型数据集对所得方法进行了评估。通过交叉验证评估的分类结果证明，所提出的方法不仅对于区分对照（NC）和AD图像有效，而且在对更具挑战性的轻度认知障碍（MCI）受试者进行分类测试时也表现良好。特别是，该分类架构对于NC/AD分类的准确率高达0.90，曲线下面积（AUC）为0.95；对于稳定MCI/AD分类的准确率为0.84，AUC为0.91；对于NC/MCI转化者分类的准确率为0.83，AUC为0.95。
