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Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach.

Jean-Baptiste Lamy1, Boomadevi Sekar2, Gilles Guezennec1

  • 1LIMICS, Université Paris 13, Sorbonne Universités, INSERM UMRS 1142, 93017 Bobigny, France.

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This study introduces a visual Case-Based Reasoning (CBR) method for breast cancer management, offering explainable AI by visualizing patient similarities. The approach achieves comparable accuracy to k-Nearest Neighbors while enhancing transparency in medical decision-making.

Keywords:
Breast cancerCase-based reasoningData-driven decision makingExplainable Artificial IntelligenceMultidimensional ScalingVisual explanation

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Data Visualization

Background:

  • Case-Based Reasoning (CBR) offers explainable alternatives to 'black box' AI models in medicine.
  • Current CBR systems often under-utilize their explanatory potential, limiting explanations to displaying similar cases.
  • Visual reasoning can enhance the interpretability of CBR outputs in clinical decision support.

Purpose of the Study:

  • To develop and evaluate a novel CBR method with a visual interface for enhanced explainability and reasoning in medical applications.
  • To combine quantitative and qualitative visualization techniques for presenting similarities between query cases and historical data.
  • To assess the classification accuracy and explainability of the proposed visual CBR method in breast cancer management.

Main Methods:

  • Developed a CBR algorithm integrated with a user interface for visual explanations.
  • Employed Multidimensional Scaling (MDS) in polar coordinates for quantitative similarity visualization.
  • Utilized set visualization with 'rainbow boxes' for qualitative similarity representation.
  • Applied and evaluated the method on public and real-world breast cancer datasets.

Main Results:

  • The visual CBR method demonstrated classification accuracy comparable to k-Nearest Neighbors algorithms on three public datasets.
  • The qualitative visualization approach proved to be more explainable than standard methods.
  • A user study indicated that medical experts found the visual approach valuable for understanding case similarities.
  • Application to a real breast cancer dataset highlighted the visualization of shared patient characteristics.

Conclusions:

  • The proposed visual CBR method provides a powerful tool for explainable AI in breast cancer management.
  • Visualizing both quantitative and qualitative similarities enhances patient classification and understanding.
  • This approach offers a transparent and interpretable alternative to traditional 'black box' algorithms in medical diagnosis and therapy.