{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
Small lesions play a critical role in early disease diagnosis and
intervention of severe infections. Popular models often face challenges in
segmenting small lesions, as it occupies only a minor portion of an image,
while down\_sampling operations may inevitably lose focus on local features of
small lesions. To tackle the challenges, we propose a {\bf S}mall-{\bf
S}ize-{\bf S}ensitive {\bf Mamba} ({\bf S$^3$-Mamba}), which promotes the
sensitivity to small lesions across three dimensions: channel, spatial, and
training strategy. Specifically, an Enhanced Visual State Space block is
designed to focus on small lesions through multiple residual connections to
preserve local features, and selectively amplify important details while
suppressing irrelevant ones through channel-wise attention. A Tensor-based
Cross-feature Multi-scale Attention is designed to integrate input image
features and intermediate-layer features with edge features and exploit the
attentive support of features across multiple scales, thereby retaining spatial
details of small lesions at various granularities. Finally, we introduce a
novel regularized curriculum learning to automatically assess lesion size and
sample difficulty, and gradually focus from easy samples to hard ones like
small lesions. Extensive experiments on three medical image segmentation
datasets show the superiority of our S$^3$-Mamba, especially in segmenting
small lesions. Our code is available at
https://github.com/ErinWang2023/S3-Mamba.
