BiSparseFusion：面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型

刘金平; 李兴旺; 刘家瑜; 刘芷娴; 刘亚琴

doi:10.12263/DZXB.20251113

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BiSparseFusion：面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型

更新时间：2026-06-15

- BiSparseFusion：面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型
- BiSparseFusion: A Cross-Modal Bidirectional Sparse Interaction Fusion Model for sMRI–fMRI-based Alzheimer’s Disease Diagnosis
- 电子学报 2026年页码：1-13
- 作者机构：
  
  1.湖南师范大学信息科学与工程学院，湖南长沙 410081
  2.湖南省智能康复机器人与辅具器械工程技术研究中心，湖南长沙 410004
  3.湖南师范大学商学院，湖南长沙 410081
  4.湖南师范大学数学与统计学院，湖南长沙 410081
- 作者简介：
  
  刘金平男，1983年9月出生于湖南省洞口县。现为湖南师范大学信息科学与工程学院教授、博士生导师。在国内外期刊发表学术论文80余篇，授权发明专利26项。主要从事大数据分析、工业故障诊断、智能监控等研究。E-mail: ljp202518@163.com
  李兴旺男，2002年5月出生于湖南省娄底市。现为湖南师范大学软件工程专业硕士研究生。研究方向为多模态医学图像分析与智能医学诊断相关研究。E-mail: lxw@hunnu.edu.cn
  刘家瑜男，2001年10月出生于湖南省洞口县。现为湖南师范大学信息科学与工程学院硕士研究生。研究方向为机器学习与智能图像处理。E-mail: 202570294510@hunnu.edu.cn
  刘芷娴女，2007年6月出生于湖南省洞口县。现为湖南师范大学商学院本科生，研究兴趣为大数据智能决策。E-mail: 202430072074@hunnu.edu.cn
  刘亚琴女，1981年12月出生于湖南省邵阳县。现为湖南师范大学数学与统计学院副教授、硕士生导师。主要从事医学图像分析、大数据智能决策相关研究。E-mail: 17873801607@163.com
- 基金信息：
  
  国家自然科学基金(62371187);湖南省智能康复机器人与辅具器械工程技术研究中心开放课题(2024JS101)
- DOI：10.12263/DZXB.20251113
  中图分类号： TP181;
- 收稿：2026-03-01，
  
  录用：2026-04-24，
  
  网络首发：2026-06-15，
- 稿件说明：
移动端阅览
刘金平, 李兴旺, 刘家瑜, 等. BiSparseFusion：面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型[J/OL]. 电子学报, 2026,1-13.

LIU Jinping, LI Xingwang, LIU Jiayu, et al. BiSparseFusion: A Cross-Modal Bidirectional Sparse Interaction Fusion Model for sMRI–fMRI-based Alzheimer’s Disease Diagnosis[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-13.
刘金平, 李兴旺, 刘家瑜, 等. BiSparseFusion：面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型[J/OL]. 电子学报, 2026,1-13. DOI： 10.12263/DZXB.20251113.

LIU Jinping, LI Xingwang, LIU Jiayu, et al. BiSparseFusion: A Cross-Modal Bidirectional Sparse Interaction Fusion Model for sMRI–fMRI-based Alzheimer’s Disease Diagnosis[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-13. DOI： 10.12263/DZXB.20251113.

摘要

阿尔茨海默病（Alzheimer’s Disease，AD）是一种隐匿且不可逆的进行性神经退行性疾病，早期精准识别对于延缓病程进展和辅助临床干预具有重要意义。结构磁共振成像（structural Magnetic Resonance Imaging，sMRI）能够反映脑萎缩、灰质退化等解剖结构异常，功能磁共振成像（functional Magnetic Resonance Imaging，fMRI）能够刻画脑区间功能活动及动态交互模式，二者从结构与功能层面为AD诊断提供互补信息。然而，面向sMRI与fMRI的多模态联合诊断仍面临三方面关键挑战：一方面，3D sMRI与4D fMRI在空间分辨率、时间维度和信号分布上存在明显差异，如何构建统一的端到端跨模态建模框架仍较困难；另一方面，基于Vision Transformer的sMRI特征提取方法虽然具有全局建模能力，但标准多头注意力机制存在注意力头冗余、头间协同不足以及局部结构细节表达不充分等问题，限制了模型对海马体、嗅皮质等AD相关关键脑区的敏感性；此外，现有多模态融合方法多采用特征拼接、单向注意力或稠密交互策略，难以在高维异构影像特征中有效筛选关键区域并建立结构—功能之间的细粒度双向关联。针对上述问题，本文提出一种面向AD诊断的sMRI-fMRI跨模态双向稀疏交互融合模型BiSparseFusion。BiSparseFusion采用双分支端到端架构，在sMRI分支中构建集成动态可组合多头注意力机制（Dynamic Composable Multi-Head Attention，DCMHA）与多层特征融合模块（Multi-level Feature Fusion Module，MFFM）的3D Vision Transformer，通过动态组合不同注意力头之间的关系降低冗余响应，并利用多层特征聚合增强局部病灶细节与全局语义信息的一致表达；在fMRI分支中引入SwiFT模型直接对原始4D fMRI进行时空依赖建模，以避免传统功能连接或感兴趣区分析法对动态信息的压缩损失；在跨模态融合阶段设计双向稀疏交叉注意力融合模块（Bidirectional Sparse Cross-Attention Fusion，BSCAF），通过模态内稀疏注意力抑制冗余特征，并通过双向交叉注意力实现sMRI结构表征与fMRI功能表征之间的深度互补交互。DCMHA与MFFM为结构分支提供更具判别性的多尺度解剖特征，SwiFT为功能分支提供时空动态特征，BSCAF则在统一特征空间内完成关键模态信息筛选、对齐与融合，从而形成结构与功能协同的AD诊断框架。本文基于ADNI数据集在AD/NC、MCI/NC和AD/MCI三个分类任务上对所提方法进行验证，实验结果表明，BiSparseFusion分别取得97.67%、93.27%和96.72%的分类准确率，优于各种单模态和多模态对比模型。可视化结果表明，模型能够聚焦海马体、嗅皮质、杏仁核等与AD病理相关的脑区，并形成类别区分度更高的融合特征空间，验证了BiSparseFusion在多模态神经影像特征建模、跨模态细粒度融合以及AD辅助诊断任务中的有效性。

Abstract

Alzheimer’s Disease (AD) is a latent and irreversible progressive neurodegenerative disorder. Early precise identification is crucial for delaying disease progression and supporting clinical intervention. Structural Magnetic Resonance Imaging (sMRI) can reveal anatomical abnormalities such as brain atrophy and gray matter degeneration

while Functional Magnetic Resonance Imaging (fMRI) captures functional activities and dynamic interactions between brain regions. Both provide complementary information for AD diagnosis from structural and functional perspectives. Multimodal joint diagnosis for sMRI and fMRI faces three key challenges. First

3D sMRI and 4D fMRI differ significantly in spatial resolution

temporal dimension

and signal distribution

making it difficult to construct a unified end-to-end cross-modal modeling framework. Second

although Vision Transformer-based sMRI feature extraction offers global modeling capabilities

the standard multi-head attention suffers from redundant heads

insufficient inter-head collaboration

and limited representation of local structural details

reducing sensitivity to AD-relevant regions such as the hippocampus and olfactory cortex. Third

most multimodal fusion methods rely on feature concatenation

unidirectional attention

or dense interaction strategies

which are insufficient to screen key regions and establish fine-grained bidirectional associations between structural and functional features in high-dimensional heterogeneous image data. To address these issues

this paper proposes BiSparseFusion

a cross-modal bidirectional sparse interaction fusion model. The sMRI branch employs a 3D Vision Transformer enhanced with a dynamic composable multi-head attention mechanism (DCMHA) and a multi-level feature fusion module (MFFM). DCMHA reduces redundant attention outputs by dynamically combining attention heads

and MFFM aggregates multi-level features to enhance local lesion details and global semantic representation. The fMRI branch uses SwiFT to directly model spatio-temporal dependencies of the original 4D fMRI

avoiding information loss caused by conventional region of interest- or connectivity-based methods. During cross-modal fusion

a bidirectional sparse cross-attention fusion module (BSCAF) suppresses redundant features within modalities and enables deep complementary interaction between sMRI structural and fMRI functional representations. The proposed method is validated on the ADNI dataset across three classification tasks: AD/NC

MCI/NC

and AD/MCI. BiSparseFusion achieves classification accuracies of 97.67%

93.27%

and 96.72%

respectively

surpassing various single- and multi-modal comparison models. Visualization results indicate that the model effectively focuses on brain regions associated with AD pathology

including the hippocampus

olfactory cortex

and amygdala

forming a more discriminative fusion feature space. These results demonstrate the effectiveness of BiSparseFusion in multi-modal neuroimaging feature modeling

cross-modal fine-grained fusion

and AD auxiliary diagnosis.

关键词

Keywords

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