Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.

翻译：准确检测阿尔茨海默病（Alzheimer's disease, AD）和预测简明精神状态检查（mini-mental state examination, MMSE）评分是磁共振成像（magnetic resonance imaging, MRI）在老年健康领域的重要任务。以往针对这两项任务的大多数方法均基于单任务学习，很少考虑二者之间的关联性。由于MMSE评分作为AD诊断的重要依据，也能反映认知障碍的进展程度，部分研究已开始将多任务学习方法应用于这两项任务。然而，如何有效利用特征相关性对这些方法而言仍是一个具有挑战性的问题。为全面应对这一挑战，我们提出了一种基于MRI的多任务解耦学习方法，用于AD检测和MMSE评分预测。首先，我们构建了一个多任务学习网络来同时实现AD检测和MMSE评分预测，该网络通过在两个任务的骨干网络之间添加三个多任务交互层来利用特征相关性。每个多任务交互层包含两个特征解耦模块和一个特征交互模块。此外，为增强特征解耦模块所选特征在任务间的泛化能力，我们提出了基于特征一致性损失约束的特征解耦模块。最后，为充分利用不同组别中MMSE评分的特定分布信息，我们引入了一个分布损失函数以进一步提升模型性能。我们在多站点数据集上评估了所提方法。实验结果表明，我们提出的多任务解耦表示学习方法取得了良好性能，优于单任务学习及其他现有最先进方法。