{S$^3$-Mamba}：用于病灶分割的小尺寸敏感Mamba
{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation
AI总结：
针对小病灶分割中降采样导致局部特征丢失的问题，提出S$^3$-Mamba模型，通过增强视觉状态空间块、基于张量的跨特征多尺度注意力及正则化课程学习，从通道、空间和训练策略三方面提升敏感性，在三个数据集上表现优越。
摘要：
小病灶在严重感染的早期疾病诊断和干预中发挥着关键作用。由于小病灶仅占据图像的极小部分，而降采样操作不可避免地会丢失对小病灶局部特征的关注，因此流行模型在分割小病灶时常常面临挑战。为了应对这些挑战，我们提出了一种小尺寸敏感Mamba（S$^3$-Mamba），它从通道、空间和训练策略三个维度提升了对小病灶的敏感性。具体而言，设计了一个增强视觉状态空间块，通过多重残差连接保留局部特征以聚焦于小病灶，并通过通道注意力选择性地放大重要细节同时抑制无关细节。设计了基于张量的跨特征多尺度注意力，将输入图像特征与中间层特征及边缘特征相融合，并利用跨多个尺度特征的注意力支持，从而在不同粒度上保留小病灶的空间细节。最后，我们引入了一种新颖的正则化课程学习，以自动评估病灶大小和样本难度，并逐渐从简单样本过渡到如小病灶等困难样本。在三个医学图像分割数据集上的大量实验表明了我们的S$^3$-Mamba的优越性，特别是在分割小病灶方面。我们的代码可在https://github.com/ErinWang2023/S3-Mamba获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
