# Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection

arXiv: https://arxiv.org/abs/2504.03230 · HTML: https://mlsanity.com/paper/2504.03230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies](https://mlsanity.com/paper/2207.11352/) (2022)
- [Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps](https://mlsanity.com/paper/2310.16936/) (2023)
- [Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision](https://mlsanity.com/paper/2402.16008/) (2024)

## Abstract (arXiv)

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.