BCT-Net: semantic-guided breast cancer segmentation on BUS.

Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundaries, and prevalent shadows in ultrasound images, tumor segmentation remains a daunting task. In this study, we propose BCT-Net, a network amalgamating CNN and transformer components for breast tumor segmentation. BCT-Net integrates a dual-level attention mechanism to capture more features and redefines the skip connection module. We introduce the utilization of a classification task as an auxiliary task to impart additional semantic information to the segmentation network, employing supervised contrastive learning. A hybrid objective loss function is proposed, which combines pixel-wise cross-entropy, binary cross-entropy, and supervised contrastive learning loss. Experimental results demonstrate that BCT-Net achieves high precision, with Pre and DSC indices of 86.12% and 88.70%, respectively. Experiments conducted on the BUSI dataset of breast ultrasound images manifest that this approach exhibits high accuracy in breast tumor segmentation.