广东工业大学学报2026，Vol.43Issue(2)：1-11,11.DOI:10.12052/gdutxb.250002
双模态迭代交叉注意力融合集成框架
Bimodal Iterative Cross-attention Fusion Ensemble Framework
摘要
Abstract
Alzheimer's disease(AD),as a progressive neurodegenerative disorder,presents significant challenges in early diagnosis and clinical intervention.In medical imaging,structural magnetic resonance imaging(sMRI)captures brain atrophy and structural alterations through high-resolution anatomical imaging,while fluorodeoxyglucose positron emission tomography(FDG-PET)effectively reflects functional changes by monitoring cerebral glucose metabolism.These two modalities hold complementary value in detecting AD-related pathological brain changes.However,existing multimodal AD classification models are limited by suboptimal feature fusion,insufficient inter-modal information interaction,and feature distribution discrepancies,hindering their diagnostic utility.To address these issues,a bimodal iterative cross-attention fusion ensemble framework(BICAFEF)is proposed.This framework comprises base classifiers and a meta-classifier.The base classifiers employ ResNet modules to extract features from sMRI and FDG-PET image patches.A spatial feature shrinking(SFS)module,integrating convolutional operations and adaptive aggregation pooling,is designed to reduce inter-modal redundancy and emphasize discriminative features.Additionally,an iterative cross-attention mechanism is constructed to dynamically capture and reinforce global dependencies and complementary information across modalities through multi-round iterations,thereby resolving the challenge of insufficiently exploiting inter-modal synergies and enhancing AD classification performance.To further improve whole-brain classification accuracy,the framework incorporates a meta-classifier to screen and ensemble base classifiers by discarding those with accuracy below 75％,retaining high-performance classifiers to boost robustness and precision.Visualization analyses validate the framework's focus on critical brain regions,demonstrating its capability to effectively identify AD-related pathological areas in sMRI and PET modalities.Experimental results show that the framework achieves a five-fold classification accuracy(ACC)of 94.3％,sensitivity(SEN)of 92.6％,specificity(SPE)of 96.3％,AUC of 97.5％,and Matthews correlation coefficient(MCC)of 88.7％in AD vs.healthy control(HC)classification,outperforming state-of-the-art multimodal frameworks.关键词
阿尔茨海默症/多模态融合/迭代学习/交叉注意力机制/分类Key words
Alzheimer's disease(AD)/multimodal fusion/iterative learning/cross-attention mechanism/classification分类
信息技术与安全科学引用本文复制引用
蔡志宏,曾安,潘丹,叶嘉宇..双模态迭代交叉注意力融合集成框架[J].广东工业大学学报,2026,43(2):1-11,11.基金项目
国家自然科学基金资助项目(61976058) （61976058）
广州市科技计划项目(202103000034,202206010007,202002020090) （202103000034,202206010007,202002020090）
广东省科技计划项目(2021A1515012300,2019A050510041,2021B0101220006) （2021A1515012300,2019A050510041,2021B0101220006）
