From redundancy to purity: A sparsity-driven feature purification network for breast ultrasound segmentation

In breast ultrasound segmentation, the accurate delineation of lesions is often severely hindered by pervasive background noise and uninformative feature responses. Existing methods typically struggle with feature redundancy during propagation, where irrelevant background information overshadows critical structures. This not only degrades segmentation performance but also incurs unnecessary computational overhead. To address this bottleneck, we propose SFPNet, a Sparsity-Driven Feature Purification Network designed to transition representations from redundancy to purity. Specifically, the Multi-Scale Dynamic Hybrid Modeling (MDHM) module is designed to comprehensively capture diverse anatomical structures and extensive multi-scale features. These representations are subsequently calibrated to evaluate feature importance and perform an initial suppression of redundancy. Secondly, we introduce an Adaptive Cross-Scale Purification Strategy (ACPS) that acts as the core purifier in the network. Driven by sparsity-guided modulation and cross-scale aggregation, ACPS helps suppress redundant responses and preserve informative structural features for decoder reconstruction. Extensive experiments are conducted on five public datasets, including three breast ultrasound datasets and two non-breast medical datasets. The results demonstrate that SFPNet achieves competitive segmentation performance while maintaining a favorable efficiency-performance balance. These findings suggest that SFPNet offers a promising lightweight framework for breast ultrasound lesion segmentation by balancing segmentation accuracy and computational efficiency. The source code of SFPNet will be publicly available at https://github.com/ccpipi/SFPNet .