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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Updated: May 30, 2025

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MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models.

Grace Guo1, Lifu Deng2, Animesh Tandon2

  • 1Georgia Institute of Technology Atlanta, Georgia, USA.

Proceedings of the ... Conference on Fairness, Accountability, and Transparency
|January 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MiMICRI, a new tool for explaining artificial intelligence (AI) in medical imaging. It helps experts validate AI cardiovascular image analysis by focusing on domain knowledge, improving trust and interpretability.

Keywords:
counterfactual explanationexplainable AIhuman-centered AIinteractive visualizations

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence
  • Explainable AI

Background:

  • Large medical imaging datasets fuel AI for cardiovascular analysis.
  • Existing explainable AI (XAI) methods often lack domain expert input and medical context.
  • This gap hinders the validation and trustworthiness of AI in clinical settings.

Purpose of the Study:

  • To introduce MiMICRI, a novel framework and Python library for domain-centered counterfactual explanations in cardiovascular image classification.
  • To enable interactive exploration and validation of AI models using medical expertise.
  • To enhance the interpretability and clinical relevance of AI predictions.

Main Methods:

  • Development of the MiMICRI Python library for creating domain-centered counterfactual explanations.
  • Interactive selection and replacement of image segments corresponding to morphological structures.
  • Evaluation of MiMICRI with two medical experts to assess interpretability and clinical plausibility.

Main Results:

  • MiMICRI enhances AI model interpretability by allowing experts to reason with domain knowledge.
  • The domain-centered XAI approach aids in validating AI predictions against medical facts.
  • Experts raised concerns regarding the clinical plausibility of generated counterfactuals.

Conclusions:

  • Domain-centered XAI, as implemented in MiMICRI, can improve AI interpretability in healthcare.
  • Further research is needed to address the clinical plausibility of counterfactuals for broader adoption.
  • The MiMICRI framework offers a pathway towards more generalizable and trustworthy AI in medical imaging.