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Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models

Limai Jiang, Ruitao Xie, Bokai Yang, Huazhen Huang, Juan He, Yufu Huo, Zikai Wang, Yang Wei, Yunpeng Cai

TL;DR

An explanation model for segmentation task is introduced which employs the causal inference framework and backpropagates the average treatment effect into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas.

Abstract

Medical image segmentation plays a vital role in clinical decision-making, enabling precise localization of lesions and guiding interventions. Despite significant advances in segmentation accuracy, the black-box nature of most deep models has raised growing concerns about their trustworthiness in high-stakes medical scenarios. Current explanation techniques have primarily focused on classification tasks, leaving the segmentation domain relatively underexplored. We introduced an explanation model for segmentation task which employs the causal inference framework and backpropagates the average treatment effect (ATE) into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas. Through comparison with recent segmentation explainability techniques on two representative medical imaging datasets, we demonstrated that our approach provides more faithful explanations than existing approaches. Furthermore, we carried out a systematic causal analysis of multiple foundational segmentation models using our method, which reveals significant heterogeneity in perceptual strategies across different models, and even between different inputs for the same model. Suggesting the potential of our method to provide notable insights for optimizing segmentation models. Our code can be found at https://github.com/lcmmai/PdCR.

Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models

TL;DR

An explanation model for segmentation task is introduced which employs the causal inference framework and backpropagates the average treatment effect into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas.

Abstract

Medical image segmentation plays a vital role in clinical decision-making, enabling precise localization of lesions and guiding interventions. Despite significant advances in segmentation accuracy, the black-box nature of most deep models has raised growing concerns about their trustworthiness in high-stakes medical scenarios. Current explanation techniques have primarily focused on classification tasks, leaving the segmentation domain relatively underexplored. We introduced an explanation model for segmentation task which employs the causal inference framework and backpropagates the average treatment effect (ATE) into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas. Through comparison with recent segmentation explainability techniques on two representative medical imaging datasets, we demonstrated that our approach provides more faithful explanations than existing approaches. Furthermore, we carried out a systematic causal analysis of multiple foundational segmentation models using our method, which reveals significant heterogeneity in perceptual strategies across different models, and even between different inputs for the same model. Suggesting the potential of our method to provide notable insights for optimizing segmentation models. Our code can be found at https://github.com/lcmmai/PdCR.
Paper Structure (25 sections, 3 equations, 8 figures, 3 tables)

This paper contains 25 sections, 3 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Medical Image Segmentation
  4. Explainability in Medical Image
  5. Causal Reasoning
  6. Method
  7. Preliminaries
  8. Intervening Black-box Models
  9. Principles of Intervention
  10. Pruning Strategy
  11. Coarse-to-Fine Patch Screening
  12. Convergence Steps
  13. Experiments
  14. Experimental Configuration
  15. Black-box Segmentation Models and Baseline Explanation Methods
  16. ...and 10 more sections

Figures (8)

  • Figure 1: The overall workflow of PdCR. (a) Given an image $X$, an RoI is selected and its initial state $M_0$ is computed. The surrounding region (gray box) may influence it. (b) Perturbations are applied to $X$, and outputs are compared to assess the causal impact. (c) Patch-wise perturbations are performed in a coarse-to-fine manner. A small set $b_N$ is first used to filter out irrelevant patches, followed by a larger set $b_N$ to compute a fine-grained causal saliency map. (d) Legend for symbols and lines.
  • Figure 2: Examples of original image and perturbations applied to its patches.
  • Figure 3: Relationship between the number of inference steps and the results, where lines of different colors represent different networks.
  • Figure 4: Visualization comparison using three explainability methods. In PdCR, green outlines denote the selected RoI.
  • Figure 5: Average attribution curves across datasets under progressive perturbation based on saliency maps.
  • ...and 3 more figures