Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretability limits clinical trust and adoption. This paper introduces a novel pre-model approach leveraging Jacobian Maps (JMs) within a multi-modal framework to enhance explainability and trustworthiness in AD detection. By capturing localized brain volume changes, JMs establish meaningful correlations between model predictions and well-known neuroanatomical biomarkers of AD. We validate JMs through experiments comparing a 3D CNN trained on JMs versus on traditional preprocessed data, which demonstrates superior accuracy. We also employ 3D Grad-CAM analysis to provide both visual and quantitative insights, further showcasing improved interpretability and diagnostic reliability.

翻译：阿尔茨海默病（AD）会导致进行性认知功能衰退，因此早期检测对于有效干预至关重要。尽管深度学习模型在AD诊断中展现出高准确率，但其缺乏可解释性限制了临床信任与采用。本文提出一种新颖的前置建模方法，在多模态框架中利用雅可比映射（JMs）以增强AD检测的可解释性与可信度。通过捕捉局部脑容量变化，JMs在模型预测与已知的AD神经解剖学生物标志物之间建立了有意义的关联。我们通过对比实验验证了JMs的有效性：训练于JMs的3D CNN相较于使用传统预处理数据的模型，展现出更优的准确率。我们还采用3D Grad-CAM分析提供视觉与定量双重洞察，进一步证明了该方法在可解释性与诊断可靠性方面的提升。