现代信息科技2026，Vol.10Issue(17)：31-35,5.DOI:10.19850/j.cnki.2096-4706.2026.17.006
基于3D注意力的混合神经网络用于脑影像认知评估
Hybrid Neural Network with 3D Attention for Brain Image-Based Cognitive Assessment
摘要
Abstract
Alzheimer's disease(AD)is a neurodegenerative disease characterized by progressive cognitive dysfunction.Clinical cognitive scores(such as the Mini-Mental State Examination and the Clinical Dementia Rating)are important criteria for characterizing disease severity,determining pathological stages,and predicting disease progression.However,traditional scale-based assessment procedures are time-consuming and are easily influenced by physicians'subjective judgment and individual differences among subjects,resulting in a certain degree of subjectivity in the assessment results.To improve the objectivity and accuracy of cognitive assessment,this study proposes a hybrid deep network based on structural magnetic resonance imaging(sMRI)to achieve automatic estimation of clinical cognitive scores.This method constructs a regression model that integrates a three-dimensional convolutional neural network(3D-CNN)with a Transformer attention mechanism for the joint prediction of multiple cognitive scores.Experiments are conducted on the ADNI dataset.The results demonstrate that the proposed method can effectively capture brain structural features related to cognitive decline and achieve stable and reliable predictive performance in cognitive score estimation tasks,providing a feasible solution for the objective assessment and assisted diagnosis of AD.关键词
认知评估/脑影像/注意力机制/神经网络Key words
cognitive assessment/brain image/attention mechanism/neural network分类
信息技术与安全科学引用本文复制引用
黄基,朱可欣,徐宾..基于3D注意力的混合神经网络用于脑影像认知评估[J].现代信息科技,2026,10(17):31-35,5.基金项目
福建省医疗大数据工程重点实验室开放课题项目(KLKF202304) （KLKF202304）
