Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
Journal:
arXiv
Published Date:
Apr 4, 2025
Abstract
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.
