北京大学学报（自然科学版）2025，Vol.61Issue(4)：629-638,10.DOI:10.13209/j.0479-8023.2024.121
基于多模态交叉注意力的阿尔茨海默症辅助诊断研究
A Multimodal Cross-Attention Model for Alzheimer's Disease Diagnosis
摘要
Abstract
In order to achieve accurate computer-aided diagnosis of Alzheimer's disease(AD)and mild cognitive impairment(MCI)patients,this paper proposes a multimodal Alzheimer's multi-class diagnostic framework(MAMDF)that uses an asymmetric cross-attention mechanism for multimodal fusion to better reveal the relationship between clinical data and medical imaging data.Moreover,to address the two MCI subtypes that are rarely mentioned in previous computer-aided diagnosis work,we combined frequency-domain transformers and Transformers to propose a novel deep feature extraction module for feature fusion.This method captures the internal connections of fused features and obtains richer multimodal joint representations,thus improving the diagnostic performance of the model on the two MCI subtypes.Experimental results on the ADNI dataset show that the proposed model achieves higher accuracy and F1 scores,compared with similar works.Thus the model can more effectively handle multimodal data fusion and mine the deep feature relationships between different modal medical data,thereby better integrating and analyzing the multimodal information of AD patients.关键词
多模态深度学习/阿尔茨海默症诊断/交叉注意力机制Key words
multi-modal deep learning/Alzheimer's disease diagnosis/cross-attention mechanism引用本文复制引用
李舟,刘永彬,欧阳纯萍,张江涛,潘雪,江璐,钟进..基于多模态交叉注意力的阿尔茨海默症辅助诊断研究[J].北京大学学报（自然科学版）,2025,61(4):629-638,10.基金项目
国家自然科学基金(61533018)、湖南省自然科学基金(2022JJ30495,2025JJ50384)、湖南省教育厅重点科研项目(22A0316)、湖南省研究生科研创新项目(CX20240833)和中国中文信息学会社会媒体处理专委会(SMP)-智谱大模型交叉学科基金资助 （61533018）
