A Second-Order Subregion Pooling Network for Breast Lesion Segmentation in Ultrasound

Breast lesion segmentation in ultrasound images is a fundamental task for clinical diagnosis of the disease. Unfortunately, existing methods mainly rely on the entire image to learn the global context information, which neglects the spatial relation and results in ambiguity in the segmentation results. In this paper, we propose a novel second-order subregion pooling network (\(S^2P\)-Net) for boosting the breast lesion segmentation in ultrasound images. In our \(S^2P\)-Net, an attention-weighted subregion pooling (ASP) module is introduced in each encoder block of segmentation network to refine features by aggregating global features from the whole image and local information of subregions. Moreover, in each subregion, a guided multi-dimension second-order pooling (GMP) block is designed to leverage additional guidance information and multiple feature dimensions to learn powerful second-order covariance representations. Experimental results on two datasets demonstrate that our proposed \(S^2P\)-Net outperforms state-of-the-art methods.