Breast Segmentation

Breast segmentation, the automated identification and delineation of breast tissue in medical images (like mammograms and MRIs), aims to improve the accuracy and efficiency of breast cancer diagnosis and treatment planning. Current research focuses on improving segmentation accuracy using advanced deep learning architectures, such as transformers and convolutional neural networks, and addressing challenges like domain adaptation (handling variations between imaging systems) and data scarcity through techniques like contrastive learning and synthetic data generation. These advancements hold significant promise for enhancing the quantification of breast density, improving the performance of computer-aided detection systems, and ultimately leading to more effective and equitable breast cancer care.