Dense Breast

Dense breast tissue, a significant risk factor for breast cancer, presents challenges for mammography screening due to its obscuring effect on potential tumors. Current research focuses on improving the accuracy of breast density classification and cancer detection in dense breasts using deep learning models, including transformer-based architectures and generative adversarial networks (GANs), often incorporating multi-modal data (mammography and ultrasound) and longitudinal imaging. These advancements aim to enhance the sensitivity and specificity of breast cancer screening, ultimately improving diagnostic accuracy and risk assessment for women with dense breasts.