Objective:

To summarize current developments in Explainable Artificial Intelligence (XAI) applied to ultrasound imaging for breast cancer diagnosis and identify significant obstacles.

Approach:

Key Findings:

• Deep learning methods have significantly improved the automation of breast cancer detection and classification in ultrasound imaging.
• The black-box nature of deep learning models poses challenges for clinical acceptance and reliability.
• XAI methods like Grad-CAM, LIME, and SHAP can enhance the interpretability of these models.

Interpretation:

Limitations:

• Lack of standardized evaluation metrics for XAI methods.
• Limited clinical validation of XAI approaches.
• Difficulty in interpreting explanations in noisy imaging conditions.

Conclusion:

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