Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework

Explainability is essential for clinical adoption: The MammoFormer framework integrates explainable AI (XAI) functionalities alongside detection capabilities, addressing a critical barrier to doctors accepting AI systems in real clinical settings. Traditional AI models often work as "black boxes," but this framework provides "multi-perspective diagnostic interpretability" so clinicians can understand why the system flagged a potential abnormality.

Different AI architectures need different preparation methods: The research tested seven different AI architectures and found that pairing them with appropriate image enhancement techniques (such as adaptive histogram equalization) improved performance by up to 13%. This suggests that one-size-fits-all approaches to AI in medical imaging may be suboptimal—customization matters.

Combining different AI approaches may be more reliable than single models: The framework uses an ensemble system that combines both traditional CNNs and newer transformer models. This hybrid approach leverages the strengths of each technology—CNN reliability with transformer models' ability to understand broader context—potentially creating more robust diagnostic tools than relying on a single AI method.

Context and detail both matter in mammography interpretation: The research highlights that previous AI systems struggled because they couldn't simultaneously process both fine local details and wider contextual information. The MammoFormer framework addresses this dual requirement, which is crucial since breast cancer abnormalities can be subtle and their significance depends on surrounding tissue context.