提出SAMA-UNet模型，兼顾医学图像分割的精度与效率。
UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
- 用自适应注意力融合局部与全局特征，提升结构表征能力。
- 在多数据集上达85.38%以上DSC，性能超越CNN、Transformer等方法。
- 适合追求高精度与低计算开销的临床医学图像分割任务。
医学图像分割在临床应用中至关重要，但现有深度学习模型在效率与精度间存在权衡。卷积神经网络（CNN）擅长捕捉局部细节，却忽略全局上下文；变压器（Transformer）能建模全局依赖，但计算成本高。近期状态空间序列模型（SSMs）以线性复杂度捕捉长程依赖，但在医学图像分割中受限于与图像结构不兼容及自回归假设。为此，本文提出SAMA-UNet，一种新型U型架构，包含两项创新：其一，自适应马比卡类聚合注意力（SAMA）模块通过动态注意力加权，高效融合局部与全局特征，增强对复杂解剖模式的表征；其二，因果共振多尺度模块（CR-MSM）通过调整跨尺度特征分辨率与因果依赖关系，提升编码器-解码器间语义对齐。在MRI、CT和内窥镜数据集上的大量实验表明，SAMA-UNet持续优于基于CNN、Transformer和马比卡的方法，在BTCV上达到85.38% DSC和87.82% NSD，ACDC上为92.16%和96.54%，EndoVis17上为67.14%和68.70%，ATLAS23上为84.06%和88.47%，建立多模态新基准。结果证实该模型在兼顾效率与精度方面的有效性，适用于真实临床分割场景。源代码已公开于GitHub。
原文摘要 · Abstract (English)
Medical image segmentation plays an important role in various clinical applications; however, existing deep learning models face trade-offs between efficiency and accuracy. Convolutional Neural Networks (CNNs) capture local details well but miss the global context, whereas transformers handle the global context but at a high computational cost. Recently, State Space Sequence Models (SSMs) have shown potential for capturing long-range dependencies with linear complexity; however, their direct use in medical image segmentation remains limited due to incompatibility with image structures and autoregressive assumptions. To overcome these challenges, we propose SAMA-UNet, a novel U-shaped architecture that introduces two key innovations. First, the Self-Adaptive Mamba-like Aggregated Attention (SAMA) block adaptively integrates local and global features through dynamic attention weighting, enabling an efficient representation of complex anatomical patterns. Second, the causal resonance multi-scale module (CR-MSM) improves encoder-decoder interactions by adjusting feature resolution and causal dependencies across scales, enhancing the semantic alignment between low- and high-level features. Extensive experiments on MRI, CT, and endoscopy datasets demonstrate that SAMA-UNet consistently outperforms CNN, Transformer, and Mamba-based methods. It achieves 85.38% DSC and 87.82% NSD on BTCV, 92.16% and 96.54% on ACDC, 67.14% and 68.70% on EndoVis17, and 84.06% and 88.47% on ATLAS23, establishing new benchmarks across modalities. These results confirm the effectiveness of SAMA-UNet in combining efficiency and accuracy, making it a promising solution for real-world clinical segmentation tasks. The source code is available on GitHub.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。