# Path-Weighted Integrated Gradients for Interpretable Dementia Classification

arXiv: https://arxiv.org/abs/2509.17491 · HTML: https://mlsanity.com/paper/2509.17491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [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)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Guided Integrated Gradients: An Adaptive Path Method for Removing Noise](https://mlsanity.com/paper/2106.09788/) (2021)
- [Attribution in Scale and Space](https://mlsanity.com/paper/2004.03383/) (2020)
- [Sequential Integrated Gradients: a simple but effective method for explaining language models](https://mlsanity.com/paper/2305.15853/) (2023)
- [Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution](https://mlsanity.com/paper/2405.09800/) (2024)
- [Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path](https://mlsanity.com/paper/2412.18844/) (2024)
- [Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision](https://mlsanity.com/paper/2305.20052/) (2023)
- [Tangentially Aligned Integrated Gradients for User-Friendly Explanations](https://mlsanity.com/paper/2503.08240/) (2025)
- [Riemannian Integrated Gradients: A Geometric View of Explainable AI](https://mlsanity.com/paper/2503.00892/) (2025)
- [Riemann Sum Optimization for Accurate Integrated Gradients Computation](https://mlsanity.com/paper/2410.04118/) (2024)

## Built on by (most cited first, 1 of 1)

- [Path-Sampled Integrated Gradients](https://mlsanity.com/paper/2604.14338/) (2026)

## Abstract (arXiv)

Integrated Gradients (IG) is a widely used attribution method in explainable artificial intelligence (XAI). In this paper, we introduce Path-Weighted Integrated Gradients (PWIG), a generalization of IG that incorporates a customizable weighting function into the attribution integral. This modification allows for targeted emphasis along different segments of the path between a baseline and the input, enabling improved interpretability, noise mitigation, and the detection of path-dependent feature relevance. We establish its theoretical properties and illustrate its utility through experiments on a dementia classification task using the OASIS-1 MRI dataset. Attribution maps generated by PWIG highlight clinically meaningful brain regions associated with various stages of dementia, providing users with sharp and stable explanations. The results suggest that PWIG offers a flexible and theoretically grounded approach for enhancing attribution quality in complex predictive models.