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Path-Sampled Integrated Gradients

Apr 15, 2026

This work addresses the high variance and poor stability of feature attribution methods caused by gradient noise. To overcome these limitations, the authors propose a deterministic attribution framework based on linear interpolation path sampling. By establishing the equivalence between path sampling and weighted integrated gradients, the method reformulates stochastic estimation as a Riemann sum, enabling efficient and stable attribution computation. Theoretical analysis demonstrates that, under smooth models, the proposed approach improves the error convergence rate from $O(m^{-1/2})$ to $O(m^{-1})$. Moreover, under uniform sampling, it rigorously reduces attribution variance by one-third while preserving both linearity and implementation invariance.

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Path-Weighted Integrated Gradients for Interpretable Dementia Classification

Sep 22, 2025

Integrated Gradients (IG) suffer from uniform sensitivity across the baseline-to-input path, vulnerability to noise, and inability to capture path-dependent features in attribution. To address these limitations, we propose Path-Weighted Integrated Gradients (PWIG), which augments the IG framework with a learnable or customizable weighting function to assign differential weights to path segments, thereby enabling selective attribution to salient regions. Evaluated on dementia classification using the OASIS-1 MRI dataset, PWIG produces attribution maps that significantly improve clinical interpretability and cross-sample stability—precisely highlighting disease-relevant brain regions (e.g., hippocampus and prefrontal cortex) while suppressing spurious noise responses. This work constitutes the first systematic integration of path-weighting into IG to enhance modeling of path-dependent attribution behavior. It establishes a novel, robust paradigm for eXplainable AI (XAI) in medical imaging, advancing both fidelity and reliability of model explanations.

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Path-Sampled Integrated Gradients

Apr 15, 2026

This work addresses the high variance and poor stability of feature attribution methods caused by gradient noise. To overcome these limitations, the authors propose a deterministic attribution framework based on linear interpolation path sampling. By establishing the equivalence between path sampling and weighted integrated gradients, the method reformulates stochastic estimation as a Riemann sum, enabling efficient and stable attribution computation. Theoretical analysis demonstrates that, under smooth models, the proposed approach improves the error convergence rate from $O(m^{-1/2})$ to $O(m^{-1})$. Moreover, under uniform sampling, it rigorously reduces attribution variance by one-third while preserving both linearity and implementation invariance.

0 citationsRead paper

Path-Weighted Integrated Gradients for Interpretable Dementia Classification

Sep 22, 2025

Integrated Gradients (IG) suffer from uniform sensitivity across the baseline-to-input path, vulnerability to noise, and inability to capture path-dependent features in attribution. To address these limitations, we propose Path-Weighted Integrated Gradients (PWIG), which augments the IG framework with a learnable or customizable weighting function to assign differential weights to path segments, thereby enabling selective attribution to salient regions. Evaluated on dementia classification using the OASIS-1 MRI dataset, PWIG produces attribution maps that significantly improve clinical interpretability and cross-sample stability—precisely highlighting disease-relevant brain regions (e.g., hippocampus and prefrontal cortex) while suppressing spurious noise responses. This work constitutes the first systematic integration of path-weighting into IG to enhance modeling of path-dependent attribution behavior. It establishes a novel, robust paradigm for eXplainable AI (XAI) in medical imaging, advancing both fidelity and reliability of model explanations.

0 citationsRead paper