Breast Lesion Segmentation

Breast lesion segmentation aims to automatically identify and delineate breast lesions in medical images (mammograms and ultrasound), assisting radiologists in diagnosis and reducing errors. Current research heavily utilizes deep learning, focusing on adapting and improving existing architectures like U-Net and the Segment Anything Model (SAM) for medical image contexts, often incorporating attention mechanisms and novel loss functions to enhance accuracy, particularly at lesion boundaries. These advancements are crucial for improving the efficiency and accuracy of breast cancer detection, potentially leading to earlier diagnosis and more effective treatment.