Efficient self-attention mechanism and structural distilling model for Alzheimer’s disease diagnosis

• Published in: Computers in biology and medicine 2022-08, Vol.147, p.105737-105737, Article 105737
• Main Authors: Zhu, Jiayi, Tan, Ying, Lin, Rude, Miao, Jiaqing, Fan, Xuwei, Zhu, Yafei, Liang, Ping, Gong, Jinnan, He, Hui
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Artificial neural networks; Atrophy; Brain; Brain architecture; Classification; Cognitive ability; Cognitive tasks; Computational complexity; Computational neuroscience; Computing costs; Deep learning; Distillation; Feature distilling; Machine learning; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Neural networks; Neurodegenerative diseases; Neuroimaging; Representations; Self-attention mechanism; Tensors
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105737

Structural magnetic resonance imaging (sMRI) is commonly used for the identification of Alzheimer’s disease because of its keen insight into atrophy-induced changes in brain structure. Current mainstream convolutional neural network-based deep learning methods ignore the long-term dependencies between voxels; thus, it is challenging to learn the global features of sMRI data. In this study, an advanced deep learning architecture called Brain Informer (BraInf) was developed based on an efficient self-attention mechanism. The proposed model integrates representation learning, feature distilling, and classifier modeling into a unified framework. First, the proposed model uses a multihead ProbSparse self-attention block for representation learning. This self-attention mechanism selects the first ⌊lnN⌋ elements that can represent the overall features from the perspective of probability sparsity, which significantly reduces computational cost. Subsequently, a structural distilling block is proposed that applies the concept of patch merging to the distilling operation. The block reduces the size of the three-dimensional tensor and further lowers the memory cost while preserving the original data as much as possible. Thus, there was a significant improvement in the space complexity. Finally, the feature vector was projected into the classification target space for disease prediction. The effectiveness of the proposed model was validated using the Alzheimer’s Disease Neuroimaging Initiative dataset. The model achieved 97.97% and 91.89% accuracy on Alzheimer’s disease and mild cognitive impairment classification tasks, respectively. The experimental results also demonstrate that the proposed framework outperforms several state-of-the-art methods. •Self-attention mechanism can capture long-term dependencies of MRI brain regions.•Structural distilling reduces memory cost and improves classification performance.•Significant performance improvement is validated compared with mainstream methods.•The proposed model used a data-driven method without relying on prior knowledge.