This letter proposes a novel way of using an ensemble of multiple deep convolutional neural networks (DCNNs) for Alzheimer's disease classification, based on magnetic resonance imaging (MRI) images. To create this ensemble of DCNNs, we propose to combine the use of multiple MRI projections (as input) with that of different DCNN architectures to increase the deep ensemble diversity. In particular, to find the optimal fusion weights of the DCNN members, we designed a novel deep ensemble generalization loss, which accounts for interaction and cooperation during the optimal weight search. The optimization framework, equipped with our ensemble generalization loss, was formulated and solved using the sequential quadratic programming. Through this method, we achieved optimal DCNN fusion weights (i.e., a high generalization performance). The experimental results showed that our proposed DCNN ensemble outperforms current deep learning-based methods: it is able to produce state-of-the-art results on the Alzheimer's disease neuroimaging initiative (ADNI) dataset.

中文翻译：

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基于 MRI 图像，通过集成泛化损失组合多个深度网络，用于阿尔茨海默病分类

这封信提出了一种基于磁共振成像 (MRI) 图像，使用多个深度卷积神经网络 (DCNN) 集合进行阿尔茨海默病分类的新方法。为了创建这个 DCNN 的集合，我们建议将多个 MRI 投影（作为输入）的使用与不同 DCNN 架构的使用相结合，以增加深度集合的多样性。特别是，为了找到 DCNN 成员的最佳融合权重，我们设计了一种新颖的深度集成泛化损失，它考虑了最佳权重搜索过程中的交互和合作。优化框架，配备了我们的集成泛化损失，使用顺序二次规划制定和解决。通过这种方法，我们实现了最佳的 DCNN 融合权重（即高泛化性能）。