标题： HCMA-UNet：一种具有跨切片自注意力的混合CNN-Mamba UNet用于高效的乳腺癌分割 显示英文标题

标题： HCMA-UNet: A Hybrid CNN-Mamba UNet with Inter-Slice Self-Attention for Efficient Breast Cancer Segmentation

摘要： 乳腺癌病灶在动态对比增强磁共振成像（DCE-MRI）中的分割仍然具有挑战性，这是由于肿瘤形态的异质性和边界不清晰。为解决这些挑战，本研究提出了一种新型的混合分割网络HCMA-UNet，用于乳腺癌病灶的分割。我们的网络包含一个轻量级的CNN主干和一个多视图跨切片自注意力马尔可夫（MISM）模块。MISM模块集成了视觉状态空间块（VSSB）和跨切片自注意力（ISSA）机制，通过非对称拆分通道（ASC）策略有效减少参数，实现高效的三向特征提取。我们的轻量级模型在拥有2.87M参数和126.44 GFLOPs的情况下取得了优越的性能。提出了一种基于特征引导的区域感知损失函数（FRLoss），以提高分割精度。在一个私有和两个公开的DCE-MRI乳腺癌数据集上的大量实验表明，我们的方法在保持计算效率的同时实现了最先进的性能。FRLoss还表现出良好的跨架构泛化能力。源代码和数据集可在该链接上获得。 显示英文摘要

摘要： Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these challenges, this study proposes a novel hybrid segmentation network, HCMA-UNet, for lesion segmentation of breast cancer. Our network consists of a lightweight CNN backbone and a Multi-view Inter-Slice Self-Attention Mamba (MISM) module. The MISM module integrates Visual State Space Block (VSSB) and Inter-Slice Self-Attention (ISSA) mechanism, effectively reducing parameters through Asymmetric Split Channel (ASC) strategy to achieve efficient tri-directional feature extraction. Our lightweight model achieves superior performance with 2.87M parameters and 126.44 GFLOPs. A Feature-guided Region-aware loss function (FRLoss) is proposed to enhance segmentation accuracy. Extensive experiments on one private and two public DCE-MRI breast cancer datasets demonstrate that our approach achieves state-of-the-art performance while maintaining computational efficiency. FRLoss also exhibits good cross-architecture generalization capabilities. The source code and dataset is available on this link.