用热力图和聚类分析揭示神经网络如何区分阿尔茨海默病患者
Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
- 通过LPR生成热力图，结合谱聚类识别分类决策模式
- 预处理与训练策略影响模型决策，即使性能相似也存在差异
- 适合关注医疗AI可解释性的研究者和临床应用开发者
深度学习模型在基于R2*图的阿尔茨海默病（AD）分类中表现优异，但其决策过程不透明，引发可解释性担忧。以往研究指出模型存在偏差，需进一步分析。本研究采用层间相关性传播（LRP）与谱聚类，探究不同预处理和训练配置下3D卷积神经网络在R2*图上的分类决策策略。通过LRP生成相关性热力图，并应用谱聚类识别主导模式。t-SNE可视化验证了聚类结构。谱聚类显示，基于相关性引导的模型在AD与正常对照（NC）病例间分离最清晰，t-SNE结果表明热力图聚类与实际受试者分组一致。研究发现，即使性能指标相近，预处理与训练选择仍显著影响模型决策方式。谱聚类提供了一种系统化方法以识别分类策略差异，凸显了医疗AI中可解释性的重要性。
原文摘要 · Abstract (English)
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural network on R2* maps, generating relevance heatmaps via LRP and applied spectral clustering to identify dominant patterns. t-Stochastic Neighbor Embedding (t-SNE) visualization was used to assess clustering structure. Spectral clustering revealed distinct decision patterns, with the relevance-guided model showing the clearest separation between AD and normal control (NC) cases. The t-SNE visualization confirmed that this model aligned heatmap groupings with the underlying subject groups. Our findings highlight the significant impact of preprocessing and training choices on deep learning models trained on R2* maps, even with similar performance metrics. Spectral clustering offers a structured method to identify classification strategy differences, emphasizing the importance of explainability in medical AI.
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