Mammographic Lesion Segmentation with Lightweight Models: A Comparative Study

ArXi:2604.23899v1 Announce Type: cross Breast cancer is a leading cause of cancer-related mortality among women worldwide, with mammography as the primary screening tool. While deep learning models have shown strong performance in lesion segmentation, most rely on computationally intensive architectures that limit their use in resource-constrained environments. This study evaluates the performance and efficiency of lightweight models for mammographic lesion segmentation.