Double-attention Assisted Multi-task Learning for the Alzheimer's Disease Prediction from Mild Cognitive Impairment

Alzheimer's disease (AD) is a progressive neurode-generative disease. Identifying the mild cognitive impairment (MCI) subjects who will convert to AD is essential for early intervention to slow the irreversible brain damage and cognitive decline. In this paper, we propose a novel double-attention assisted multi-task framework for the MCI conversion prediction task. By introducing an auxiliary grey matter segmentation task along with an adaptive dynamic weight average strategy to balance the impact of each task. Then, a double-attention module is incorporated to leverage both the classification and the segmentation attention information to guide the network to focus more on the structural alteration regions for better discrimination of AD pathology, as well as increase the interpretability of the network. Extensive experiments on a publicly available dataset demonstrate that the proposed method significantly outperforms the approaches using the same image modality.