# Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision

arXiv: https://arxiv.org/abs/2402.16008 · HTML: https://mlsanity.com/paper/2402.16008/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI](https://mlsanity.com/paper/2008.04024/) (2020)
- [Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans](https://mlsanity.com/paper/2011.14139/) (2020)
- [Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps](https://mlsanity.com/paper/2310.16936/) (2023)
- [Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images](https://mlsanity.com/paper/2107.08673/) (2021)
- [A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition](https://mlsanity.com/paper/2308.06401/) (2023)

## Built on by (most cited first, 3 of 3)

- [Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2607.20691/) (2026)
- [Efficient Brain Imaging Analysis for Alzheimer's and Dementia Detection Using Convolution-Derivative Operations](https://mlsanity.com/paper/2411.13490/) (2024)
- [Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection](https://mlsanity.com/paper/2504.03230/) (2025)

## Abstract (arXiv)

The evolution of deep learning and artificial intelligence has significantly reshaped technological landscapes. However, their effective application in crucial sectors such as medicine demands more than just superior performance, but trustworthiness as well. While interpretability plays a pivotal role, existing explainable AI (XAI) approaches often do not reveal em Clever Hans behavior where a model makes (ungeneralizable) correct predictions using spurious correlations or biases in data. Likewise, current post-hoc XAI methods are susceptible to generating unjustified counterfactual examples. In this paper, we approach XAI with an innovative em model debugging methodology realized through Jacobian Saliency Map (JSM). To cast the problem into a concrete context, we employ Alzheimer's disease (AD) diagnosis as the use case, motivated by its significant impact on human lives and the formidable challenge in its early detection, stemming from the intricate nature of its progression. We introduce an interpretable, multimodal model for AD classification over its multi-stage progression, incorporating JSM as a modality-agnostic tool that provides insights into volumetric changes indicative of brain abnormalities. Our extensive evaluation including ablation study manifests the efficacy of using JSM for model debugging and interpretation, while significantly enhancing model accuracy as well.