Breast cancer is one of the leading causes of cancer-related mortality worldwide. A large body of research has demonstrated that mammographic screening is effective in reducing both the incidence and mortality of breast cancer. To improve screening efficiency and clinical outcomes, many countries have established systematic breast cancer screening programs to promote early detection and timely intervention. However, many computer-aided detection (CAD) technologies provide only limited improvements in diagnostic accuracy, highlighting shortcomings in feature representation and decision interpretability. Logic Tensor Networks (LTNs), a neurosymbolic framework that combines neural networks with logical reasoning, has shown strong potential in incorporating domain-specific prior knowledge and performing structured prediction tasks.