U-MAN提升医学图像分割边界精度，专治细小结构丢失问题。
U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
- 用注意力融合替代简单跳跃连接，弥合编码器与解码器语义差距。
- 多尺度自适应KAN模块在深层网络中实现多尺度特征提取。
- 在三个公开数据集上显著优于现有方法，尤其擅长保留细小结构。
医学图像分割因复杂的解剖结构和病灶区域，面临难以保持细节与精确边界的挑战。传统U-Net架构存在两大局限：(1) 简单跳跃连接忽略编码器与解码器间特征的语义差异；(2) 深层缺乏多尺度特征提取能力。为此，我们提出U-MAN：一种结合多尺度自适应KAN（MAN）的新型U-Net架构。其核心包含两个模块：渐进式注意力引导特征融合（PAGF）取代原始跳跃连接，通过注意力机制融合编码器与解码器特征；以及多尺度自适应KAN（MAN），实现深层特征的自适应多尺度处理。在三个公开数据集（BUSI、GLAS、CVC）上的实验表明，U-MAN超越当前最优方法，尤其在边界定义准确性和细粒度细节保留方面表现突出。
原文摘要 · Abstract (English)
Medical image segmentation faces significant challenges in preserving fine-grained details and precise boundaries due to complex anatomical structures and pathological regions. These challenges primarily stem from two key limitations of conventional U-Net architectures: (1) their simple skip connections ignore the encoder-decoder semantic gap between various features, and (2) they lack the capability for multi-scale feature extraction in deep layers. To address these challenges, we propose the U-Net with Multi-scale Adaptive KAN (U-MAN), a novel architecture that enhances the emerging Kolmogorov-Arnold Network (KAN) with two specialized modules: Progressive Attention-Guided Feature Fusion (PAGF) and the Multi-scale Adaptive KAN (MAN). Our PAGF module replaces the simple skip connection, using attention to fuse features from the encoder and decoder. The MAN module enables the network to adaptively process features at multiple scales, improving its ability to segment objects of various sizes. Experiments on three public datasets (BUSI, GLAS, and CVC) show that U-MAN outperforms state-of-the-art methods, particularly in defining accurate boundaries and preserving fine details.
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