用雅可比图揭示脑部变化，让阿尔茨海默病诊断更可信
Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
- 通过雅可比图捕捉局部脑体积变化，关联模型预测与已知病理特征
- 基于雅可比图训练的3D CNN准确率优于传统预处理数据
- 结合3D Grad-CAM实现可视化解释，提升临床可读性
阿尔茨海默病（AD）导致认知能力渐进性衰退，早期检测对有效干预至关重要。尽管深度学习模型在AD诊断中表现出高准确率，但其缺乏可解释性限制了临床信任与应用。本文提出一种新颖的预处理方法，将雅可比图（JMs）引入多模态框架，以增强AD检测中的可解释性与可信度。通过捕捉局部脑体积变化，JMs建立了模型预测与已知神经解剖学生物标志物之间的有意义关联。实验对比了基于雅可比图与传统预处理数据训练的3D CNN，结果表明前者具有更高准确率。同时，采用3D Grad-CAM分析提供视觉与定量双重洞察，进一步验证了方法的可解释性与诊断可靠性。
原文摘要 · Abstract (English)
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.
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