Dear colleagues,

I'm currently exploring the evolving role of attention-based architectures in medical image classification. A question I’d like to open for discussion:

To what extent can attention-driven architectures outperform traditional deep classifiers in complex medical imaging tasks where data is limited, noisy, or highly imbalanced—and can they offer better interpretability, robustness, and clinical relevance?

I’m particularly curious about:

Specific imaging domains (e.g., retinal, histopathology, etc.) where attention models excel

Theoretical or empirical evidence of generalization and robustness

Interpretability trade-offs between attention-based and convolutional systems

Looking forward to your insights and experiences—whether from practical applications or recent literature.

Best regards,

S. Fouad