HyM-UNet: 通过混合CNN-Mamba架构协同局部纹理和全局上下文进行医学图像分割
HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation
AI总结：
HyM-UNet通过混合CNN-Mamba架构，结合局部纹理和全局上下文，提升医学图像分割的精度与效率。
摘要：
准确的器官和病变分割是计算机辅助诊断的关键前提。卷积神经网络（CNNs）受限于其局部感受野，往往难以捕捉复杂的全局解剖结构。为解决这一挑战，本文提出了一种新的混合架构HyM-UNet，旨在协同CNN的局部特征提取能力与Mamba的高效全局建模能力。具体而言，我们设计了一个分层编码器，利用浅层阶段的卷积模块保留高频纹理细节，同时在深层阶段引入视觉Mamba模块以捕捉长距离语义依赖关系，具有线性复杂度。为了弥合编码器和解码器之间的语义差距，我们提出了Mamba引导融合跳连接（MGF-Skip）。该模块利用深度语义特征作为门控信号，动态抑制浅层特征中的背景噪声，从而增强模糊边界的感知。我们在公开基准数据集ISIC 2018上进行了广泛的实验。结果表明，HyM-UNet在Dice系数和IoU方面显著优于现有最先进方法，同时保持较低的参数计数和推理延迟。这验证了所提出方法在处理具有复杂形状和尺度变化的医学分割任务中的有效性和鲁棒性。
英文摘要：
Accurate organ and lesion segmentation is a critical prerequisite for computer-aided diagnosis. Convolutional Neural Networks (CNNs), constrained by their local receptive fields, often struggle to capture complex global anatomical structures. To tackle this challenge, this paper proposes a novel hybrid architecture, HyM-UNet, designed to synergize the local feature extraction capabilities of CNNs with the efficient global modeling capabilities of Mamba. Specifically, we design a Hierarchical Encoder that utilizes convolutional modules in the shallow stages to preserve high-frequency texture details, while introducing Visual Mamba modules in the deep stages to capture long-range semantic dependencies with linear complexity. To bridge the semantic gap between the encoder and the decoder, we propose a Mamba-Guided Fusion Skip Connection (MGF-Skip). This module leverages deep semantic features as gating signals to dynamically suppress background noise within shallow features, thereby enhancing the perception of ambiguous boundaries. We conduct extensive experiments on public benchmark dataset ISIC 2018. The results demonstrate that HyM-UNet significantly outperforms existing state-of-the-art methods in terms of Dice coefficient and IoU, while maintaining lower parameter counts and inference latency. This validates the effectiveness and robustness of the proposed method in handling medical segmentation tasks characterized by complex shapes and scale variations.
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。
