融合QSM与T1w影像，提升帕金森病诊断准确率
GateFuseNet: An Adaptive 3D Multimodal Neuroimaging Fusion Network for Parkinson's Disease Diagnosis
- 设计门控融合模块，动态调节多模态特征权重
- 达85.00%准确率与92.06%AUC，优于现有方法
- 聚焦病变区域，适合临床辅助诊断研究者
帕金森病（PD）的磁共振成像（MRI）诊断因症状差异和病理异质性而困难。现有方法多依赖对PD病理不敏感的传统强度图像（如T1加权像，T1w），而基于相位的定量磁化率成像（QSM）能更灵敏地量化深部灰质核团中铁沉积。本文提出GateFuseNet，一种自适应3D多模态融合网络，整合QSM与T1w图像用于PD诊断。核心创新为门控融合模块，可学习模态特异性注意力权重与通道门控向量，实现特征选择性调制。该分层门控机制增强感兴趣区域（ROI）相关特征，抑制无关信号。实验表明，本方法超越三种先进模型，达到85.00%准确率和92.06% AUC。消融实验证实了ROI引导、多模态融合及融合位置的有效性。Grad-CAM可视化显示模型聚焦于临床相关病变区域。源代码与预训练模型见https://github.com/YangGaoUQ/GateFuseNet。
原文摘要 · Abstract (English)
Accurate diagnosis of Parkinson's disease (PD) from MRI remains challenging due to symptom variability and pathological heterogeneity. Most existing methods rely on conventional magnitude-based MRI modalities, such as T1-weighted images (T1w), which are less sensitive to PD pathology than Quantitative Susceptibility Mapping (QSM), a phase-based MRI technique that quantifies iron deposition in deep gray matter nuclei. In this study, we propose GateFuseNet, an adaptive 3D multimodal fusion network that integrates QSM and T1w images for PD diagnosis. The core innovation lies in a gated fusion module that learns modality-specific attention weights and channel-wise gating vectors for selective feature modulation. This hierarchical gating mechanism enhances ROI-aware features while suppressing irrelevant signals. Experimental results show that our method outperforms three existing state-of-the-art approaches, achieving 85.00% accuracy and 92.06% AUC. Ablation studies further validate the contributions of ROI guidance, multimodal integration, and fusion positioning. Grad-CAM visualizations confirm the model's focus on clinically relevant pathological regions. The source codes and pretrained models can be found at https://github.com/YangGaoUQ/GateFuseNet
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