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MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study

Shakiba Rahimiaghdam, Hande Alemdar

TL;DR

MindfulLIME addresses the instability of perturbation-based explanations in medical imaging by replacing random sample generation with a deterministic, graph-guided sampling approach. It builds a superpixel graph, applies a two-phase purposive sampling process with uncertainty-based decisions, and uses class-thresholds to ensure in-distribution samples, achieving 100% stability and improved localization on chest X-ray datasets. The method demonstrates superior localization precision and efficiency across segmentation algorithms, without requiring additional training data, suggesting strong potential for trustworthy explanations in critical healthcare settings and broader applicability to image-domain XAI. Overall, MindfulLIME elevates the reliability and interpretability of black-box models in medical imaging by combining graph-constrained sampling with uncertainty-aware sample selection and evaluation against robust localization metrics.

Abstract

Ensuring transparency in machine learning decisions is critically important, especially in sensitive sectors such as healthcare, finance, and justice. Despite this, some popular explainable algorithms, such as Local Interpretable Model-agnostic Explanations (LIME), often produce unstable explanations due to the random generation of perturbed samples. Random perturbation introduces small changes or noise to modified instances of the original data, leading to inconsistent explanations. Even slight variations in the generated samples significantly affect the explanations provided by such models, undermining trust and hindering the adoption of interpretable models. To address this challenge, we propose MindfulLIME, a novel algorithm that intelligently generates purposive samples using a graph-based pruning algorithm and uncertainty sampling. MindfulLIME substantially improves the consistency of visual explanations compared to random sampling approaches. Our experimental evaluation, conducted on a widely recognized chest X-ray dataset, confirms MindfulLIME's stability with a 100% success rate in delivering reliable explanations under identical conditions. Additionally, MindfulLIME improves the localization precision of visual explanations by reducing the distance between the generated explanations and the actual local annotations compared to LIME. We also performed comprehensive experiments considering various segmentation algorithms and sample numbers, focusing on stability, quality, and efficiency. The results demonstrate the outstanding performance of MindfulLIME across different segmentation settings, generating fewer high-quality samples within a reasonable processing time. By addressing the stability limitations of LIME in image data, MindfulLIME enhances the trustworthiness and interpretability of machine learning models in specific medical imaging applications, a critical domain.

MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study

TL;DR

MindfulLIME addresses the instability of perturbation-based explanations in medical imaging by replacing random sample generation with a deterministic, graph-guided sampling approach. It builds a superpixel graph, applies a two-phase purposive sampling process with uncertainty-based decisions, and uses class-thresholds to ensure in-distribution samples, achieving 100% stability and improved localization on chest X-ray datasets. The method demonstrates superior localization precision and efficiency across segmentation algorithms, without requiring additional training data, suggesting strong potential for trustworthy explanations in critical healthcare settings and broader applicability to image-domain XAI. Overall, MindfulLIME elevates the reliability and interpretability of black-box models in medical imaging by combining graph-constrained sampling with uncertainty-aware sample selection and evaluation against robust localization metrics.

Abstract

Ensuring transparency in machine learning decisions is critically important, especially in sensitive sectors such as healthcare, finance, and justice. Despite this, some popular explainable algorithms, such as Local Interpretable Model-agnostic Explanations (LIME), often produce unstable explanations due to the random generation of perturbed samples. Random perturbation introduces small changes or noise to modified instances of the original data, leading to inconsistent explanations. Even slight variations in the generated samples significantly affect the explanations provided by such models, undermining trust and hindering the adoption of interpretable models. To address this challenge, we propose MindfulLIME, a novel algorithm that intelligently generates purposive samples using a graph-based pruning algorithm and uncertainty sampling. MindfulLIME substantially improves the consistency of visual explanations compared to random sampling approaches. Our experimental evaluation, conducted on a widely recognized chest X-ray dataset, confirms MindfulLIME's stability with a 100% success rate in delivering reliable explanations under identical conditions. Additionally, MindfulLIME improves the localization precision of visual explanations by reducing the distance between the generated explanations and the actual local annotations compared to LIME. We also performed comprehensive experiments considering various segmentation algorithms and sample numbers, focusing on stability, quality, and efficiency. The results demonstrate the outstanding performance of MindfulLIME across different segmentation settings, generating fewer high-quality samples within a reasonable processing time. By addressing the stability limitations of LIME in image data, MindfulLIME enhances the trustworthiness and interpretability of machine learning models in specific medical imaging applications, a critical domain.

Paper Structure

This paper contains 13 sections, 4 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. LIME
  5. MindfulLIME
  6. Experimental Setup
  7. Datasets
  8. Training The Classifier
  9. Evaluation Metrics
  10. Results and Discussion
  11. Qualitative Analysis
  12. Quantitative Analysis
  13. Conclusions and Future Work

Figures (9)

  • Figure 1: A block diagram illustrating the high-level structure of the LIME framework
  • Figure 2: A block diagram illustrating the high-level structure of the MindfulLIME framework
  • Figure 3: Samples of the VinDr-CXR dataset with precise annotated labels
  • Figure 4: Visual Analysis: Comparing generated explanations (the yellow-outlined areas enclosed with light green bounding boxes) by MindfulLIME and LIME to the ground truth annotations (dark green bounding boxes) for Random Sample 1
  • Figure 5: Visual Analysis: Comparing generated explanations (the yellow-outlined areas enclosed with light blue bounding boxes) by MindfulLIME and LIME to the ground truth annotations (dark blue bounding boxes) for Random Sample 2
  • ...and 4 more figures