Breast Tumor Segmentation

Breast tumor segmentation, the automated identification of tumor boundaries in medical images, aims to improve the accuracy and efficiency of breast cancer diagnosis and treatment planning. Current research heavily utilizes deep learning, employing architectures like U-Net and transformers, often enhanced with attention mechanisms and incorporating techniques such as semi-supervised learning and multi-modal data fusion (e.g., combining MRI sequences). These advancements are improving segmentation accuracy across various imaging modalities (ultrasound, MRI) and contributing to more precise diagnoses, potentially leading to better patient outcomes and personalized treatment strategies.