Detecting vasodilation for early breast cancer using ViT-based distribution embedding via extreme value theory on deep matrix approximation

Key Points

• Vasodilation detection shows early signs of breast cancer through novel embedding techniques.
• Using extreme value theory, the method provides a new way to model data for better accuracy.
• The analysis employs ViT-based distribution embedding for deep matrix approximation techniques.
• Highlights a novel approach that may enable earlier intervention for breast cancer with upcoming validation.