Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer’s disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their prevalence, existing methods usually suffer from using either irrelevant brain regions or less-accurate landmarks. In this paper, we propose the iterative sparse and deep learning (ISDL) model for joint deep feature extraction and critical cortical region identification to diagnose AD and MCI. We first design a deep feature extraction (DFE) module to capture the local-to-global structural information derived from 62 cortical regions. Then we design a sparse regression module to identify the critical cortical regions and integrate it into the DFE module to exclude irrelevant cortical regions from the diagnosis process. The parameters of the two modules are updated alternatively and iteratively in an end-to-end manner. Our experimental results suggest the ISDL model provides a state-of-the-art solution to both AD-CN classification and MCI-to-AD prediction.

深度学习技术已越来越多地应用于阿尔茨海默氏病（AD）的诊断以及从轻度认知障碍（MCI）到AD的转化。尽管它们很普遍，但是现有方法通常会使用不相关的大脑区域或较不准确的地标。在本文中，我们提出了用于联合深度特征提取和关键皮层区域识别的迭代稀疏和深度学习（ISDL）模型，以诊断AD和MCI。我们首先设计一个深度特征提取（DFE）模块，以捕获来自62个皮质区域的局部到全局结构信息。然后，我们设计一个稀疏回归模块来识别关键皮质区域，并将其集成到DFE模块中，以从诊断过程中排除无关的皮质区域。两个模块的参数以端到端的方式交替和迭代地更新。我们的实验结果表明，ISDL模型为AD-CN分类和MCI-to-AD预测提供了最新的解决方案。