用于阿尔茨海默病诊断的MRI与PET协同注意力与一致性引导融合
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
- School of Software Yunnan University（软件学院 云南大学）
- Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）
- Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出协同注意力与一致性引导融合框架，通过LPR模块、共享与独立编码器及分布对齐机制，有效融合MRI和PET的多模态特征以提升AD诊断性能。
AI中文摘要：
阿尔茨海默病（AD）是最普遍的痴呆症形式，其早期诊断对于减缓疾病进展至关重要。近期利用MRI和PET进行多模态神经影像融合的研究，通过整合多尺度互补特征取得了令人鼓舞的成果。然而，现有大多数方法主要强调跨模态互补性，却忽略了模态特定特征的诊断重要性。此外，模态间固有的分布差异常导致有偏和含噪的表示，从而降低分类性能。为应对这些挑战，我们提出了一种用于MRI和PET的协同注意力与一致性引导融合框架。该模型引入可学习参数表示（LPR）模块以补偿缺失模态信息，随后采用共享编码器和模态独立编码器来保留共享与特定表示。此外，采用一致性引导机制显式对齐跨模态的潜在分布。在ADNI数据集上的实验结果表明，与现有融合策略相比，该方法实现了更优的诊断性能。
英文摘要：
Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fusion using MRI and PET have achieved promising results by integrating multi-scale complementary features. However, most existing approaches primarily emphasize cross-modal complementarity while overlooking the diagnostic importance of modality-specific features. In addition, the inherent distributional differences between modalities often lead to biased and noisy representations, degrading classification performance. To address these challenges, we propose a Collaborative Attention and Consistent-Guided Fusion framework for MRI and PET based AD diagnosis. The proposed model introduces a learnable parameter representation (LPR) block to compensate for missing modality information, followed by a shared encoder and modality-independent encoders to preserve both shared and specific representations. Furthermore, a consistency-guided mechanism is employed to explicitly align the latent distributions across modalities. Experimental results on the ADNI dataset demonstrate that our method achieves superior diagnostic performance compared with existing fusion strategies.
