Method for Reducing MCI Misclassification Rate Based on Cross-modal Prototype Generation

As an early stage of Alzheimer's disease (AD), the accurate detection of mild cognitive impairment (MCI) is very important for its early intervention. Due to the small between-group differences, it is easy to cause misclassification of MCI. Intrigued by the idea of metric learning, a novel framework based on cross-modal prototype generation is proposed to reduce the misclassification rate of MCI. First, the single modal prototypes are generated from magnetic resonance images (MRI), and then the auxiliary modal is used to correct the original prototypes and generate new cross-modal prototypes. An adaptive mechanism that can dynamically adjust the proportion of the auxiliary modal data is added in the proposed framework to improve the practicability of the cross-modal prototype framework. Experiments show that the cross-modal prototype generation framework can significantly reduce the misclassification rate of MCI by 40.3%.