[论文解读] MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Xu Tian, Jin Liu+4 more|arXiv (Cornell University)|Apr 2, 2022

Brain Tumor Detection and Classification被引用 4

一句话总结

该论文提出了一种基于MRI的多任务解耦学习框架，用于阿尔茨海默病检测与MMSE评分预测，通过三个多任务交互层实现共享特征表示，并结合特征解耦与一致性损失以增强泛化能力。该方法在多中心MRI数据集上优于单任务学习及现有最先进方法。

ABSTRACT

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.

研究动机与目标

• 为解决现有基于MRI的方法在阿尔茨海默病检测与MMSE评分预测之间相关性利用不足的问题。
• 通过一致性损失显式解耦共享特征与任务特定特征，以提升模型泛化能力。
• 通过建模不同认知障碍群体中MMSE评分的分布，进一步提升预测性能。
• 在多中心MRI数据集上验证该方法，以确保其鲁棒性与临床适用性。

提出的方法

• 设计了一个多任务学习网络，AD检测与MMSE预测共享主干网络，并通过三个多任务交互层连接。
• 每个多任务交互层包含两个特征解耦模块与一个特征交互模块，用于建模跨任务依赖关系。
• 引入特征一致性损失以正则化解耦后的特征，提升任务间的泛化能力。
• 提出分布损失以捕捉不同群体中MMSE评分的分布特征，提升回归精度。
• 在多中心MRI数据集上端到端训练模型，以增强对领域偏移的鲁棒性。

实验结果

研究问题

• RQ1 如何有效解耦共享特征与任务特定特征，以提升联合AD检测与MMSE预测的性能？
• RQ2 在多中心MRI环境下，强制任务间特征一致性在多大程度上能提升模型泛化能力？
• RQ3 建模不同认知障碍群体中MMSE评分的分布是否能进一步提升预测性能？
• RQ4 所提出方法在多中心数据上与单任务学习及现有最先进多任务方法相比表现如何？

主要发现

• 所提出的多任务解耦学习方法在AD检测与MMSE预测任务上均优于单任务学习方法。
• 引入特征一致性损失显著提升了共享表示在不同任务间的泛化能力。
• 分布损失通过捕捉认知衰退的群体差异，显著提升了MMSE评分预测的准确性。
• 该模型在来自不同中心的多个独立MRI数据集上表现出强大的鲁棒性与泛化能力。