使用3D MRI和PET的多模态方法诊断阿尔茨海默病
Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
AI总结：
提出结合3D卷积特征提取器与三种融合策略（拼接、门控多模态单元、门控自注意力）及稀疏门控混合专家分类器的多模态模型，用于阿尔茨海默病诊断，在三个二分类任务上验证了输入自适应建模的有效性。
摘要：
阿尔茨海默病（AD）是一种不可逆的神经退行性疾病，也是全球主要的死亡原因之一。早期诊断尤为重要，尤其是在轻度认知障碍（MCI）阶段，及时干预有助于延缓其向AD的进展。神经影像数据，如磁共振成像（MRI）和正电子发射断层扫描（PET），可以通过提供与疾病相关的结构和功能脑变化来帮助早期检测脑部变化。然而，许多多模态模型仍通过静态拼接融合MRI和PET，并对所有受试者应用相同的计算，这限制了其对患者/站点异质性的鲁棒性，并可能浪费计算资源。为解决这些局限性，我们首次研究了将3D卷积特征提取器与三种融合策略（拼接、门控多模态单元（GMU）和门控自注意力）以及一个稀疏门控混合专家（MoE）分类器相结合的方法，该分类器执行输入自适应路由，仅激活每个病例中最具信息量的专家。最后，我们利用Grad-CAM可视化疾病相关区域，确保模型的可解释性。实验在三个二分类任务（NC vs. MCI、MCI vs. AD和NC vs. AD）上进行。结果表明，GMU在NC vs. MCI和NC vs. AD上分别达到80.46%和95.47%的准确率，而门控自注意力在MCI vs. AD上达到82.08%。消融实验表明，移除MoE会持续降低所有任务的准确率。这些发现强调了利用MRI和PET互补性的输入自适应多模态建模在AD诊断中的价值。
英文摘要：
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide. Early diagnosis plays an important part especially at the Mild Cognitive Impairment stage, where timely intervention can help slow its progression before it advances to AD. Neuroimaging data, like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans, can help detect brain changes early by providing structural and functional brain changes related to the disease. Yet, many multimodal models still fuse MRI and PET with static concatenation and apply identical computation to all subjects, which limits robustness to patient/site heterogeneity and can waste computation. To address these limitations, we present the first study of combining 3D convolutional feature extractors with three fusion strategies - concatenation, Gated Multimodal Unit (GMU), and gated self-attention - and a sparsely gated Mixture-of-Experts (MoE) classifier that performs input-adaptive routing, activating only the most informative experts per case. Finally, we utilize Grad-CAM to visualize disease-related regions, ensuring model interpretability. Experiments are performed across three binary classification tasks (NC vs. MCI, MCI vs. AD, and NC vs. AD). Results show that GMU achieves accuracies of 80.46 % (NC vs. MCI) and 95.47 % (NC vs. AD), while gated self-attention attains 82.08 % on MCI vs. AD. Ablations show that removing the MoE consistently degrades accuracy across all tasks. These findings underscore the value of input-adaptive, multimodal modeling for AD diagnosis by leveraging the complementary nature of MRI and PET.
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