ACM-UNet：自适应集成CNNs和Mamba的高效医学图像分割
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
AI总结：
本文提出ACM-UNet，通过轻量级适配器机制整合预训练CNNs和Mamba模型，并在解码器引入分层多尺度小波变换模块，在Synapse和ACDC基准上实现了计算高效的最先进医学图像分割性能。
摘要：
带有跳跃连接的U型编码器-解码器架构因其简单性和有效性，已成为医学图像分割中的主流范式。尽管许多近期工作试图通过设计更强大的编码器和解码器来改进此框架，例如采用先进的卷积神经网络（CNNs）进行局部特征提取，使用Transformers或状态空间模型（SSMs，如Mamba）进行全局上下文建模，或两者的混合组合，但由于结构不匹配，这些方法通常难以充分利用预训练的视觉主干网络（如ResNet、ViT、VMamba）。为了弥补这一差距，我们引入了ACM-UNet，这是一个通用的分割框架，它保留了简单的类UNet设计，同时通过轻量级适配器机制有效整合了预训练的CNNs和Mamba模型。该适配器解决了架构不兼容问题，并使模型能够利用CNNs和SSMs的互补优势，即细粒度的局部细节提取和长距离依赖建模。此外，我们在解码器中提出了一个分层多尺度小波变换模块，以增强特征融合和重建保真度。在Synapse和ACDC基准上的大量实验表明，ACM-UNet在保持计算效率的同时实现了最先进的性能。值得注意的是，它在Synapse数据集上达到了85.12%的Dice分数和13.89mm的HD95，FLOPs为17.93G，展示了其有效性和可扩展性。代码可在以下网址获取：https://github.com/zyklcode/ACM-UNet。
英文摘要：
The U-shaped encoder-decoder architecture with skip connections has become a prevailing paradigm in medical image segmentation due to its simplicity and effectiveness. While many recent works aim to improve this framework by designing more powerful encoders and decoders, employing advanced convolutional neural networks (CNNs) for local feature extraction, Transformers or state space models (SSMs) such as Mamba for global context modeling, or hybrid combinations of both, these methods often struggle to fully utilize pretrained vision backbones (e.g., ResNet, ViT, VMamba) due to structural mismatches. To bridge this gap, we introduce ACM-UNet, a general-purpose segmentation framework that retains a simple UNet-like design while effectively incorporating pretrained CNNs and Mamba models through a lightweight adapter mechanism. This adapter resolves architectural incompatibilities and enables the model to harness the complementary strengths of CNNs and SSMs-namely, fine-grained local detail extraction and long-range dependency modeling. Additionally, we propose a hierarchical multi-scale wavelet transform module in the decoder to enhance feature fusion and reconstruction fidelity. Extensive experiments on the Synapse and ACDC benchmarks demonstrate that ACM-UNet achieves state-of-the-art performance while remaining computationally efficient. Notably, it reaches 85.12% Dice Score and 13.89mm HD95 on the Synapse dataset with 17.93G FLOPs, showcasing its effectiveness and scalability. Code is available at: https://github.com/zyklcode/ACM-UNet.
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。
