Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) is a 3D breast imaging technique offering improved cancer detection compared to traditional mammography, but faces challenges in data acquisition and analysis. Current research focuses on developing robust and efficient computer-aided detection (CAD) systems using deep learning architectures like convolutional neural networks (CNNs) and transformers, often employing semi-supervised learning and techniques like knowledge distillation to address data scarcity and annotation costs. These advancements aim to improve the accuracy and efficiency of breast cancer diagnosis, potentially reducing interpretation times and radiation exposure while enhancing the overall effectiveness of breast cancer screening.