Abstract Breast cancer remains a leading cause of mortality among women worldwide, with early detection via mammography significantly improving patient outcomes. Automated segmentation of mammographic lesions using deep learning can enhance diagnostic efficiency; however, existing methods face critical challenges: (1) severe class imbalance (< 2% foreground pixels), (2) small lesion sizes (3–15 mm), and (3) limited annotated datasets, which hinder clinical applicability. To overcome these limitations, we propose SE-Attention U-Net, a hybrid framework featuring squeeze-and-excitation blocks for adaptive feature refinement, attention gates to focus on salient regions, and a novel loss function explicitly designed to address extreme class imbalance.We evaluated our approach on the publicly available CBIS-DDSM dataset, a widely recognized benchmark in mammography research. Our model achieved state-of-the-art performance with a Dice coefficient of 91.00%, Jaccard coefficient of 86.01%, accuracy of 99.54%, precision of 97.97%, sensitivity of 97.08%, and an F1-score of 97.53%. These results demonstrate robust lesion localization and minimized false positives, outperforming existing methods. The proposed framework shows significant potential for clinical integration, providing radiologists with a reliable tool for early and accurate segmentation of small breast lesions.
Maliheh Habibi (Mon,) studied this question.