  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.
