解码至关重要：基于Mamba的高效解码器与分布感知深度监督用于医学图像分割
Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
AI总结：
本文提出基于解码器的通用2D医学图像分割方法，通过改进的解码器结构和分布感知损失提升多尺度上下文表示，实现高效且通用的医学图像分割。
摘要：
深度学习在医学图像分割中取得了显著成功，能够达到专家水平的肿瘤和组织界定精度。然而，现有方法大多任务特定，性能优异但泛化能力有限。本文提出一种以解码器为中心的通用2D医学图像分割方法。Deco-Mamba采用U-Net-like结构，结合Transformer-CNN-Mamba设计。编码器结合CNN块和Transformer主干进行高效特征提取，解码器集成新的Co-Attention Gate (CAG)、Vision State Space Module (VSSM)和可变形卷积细化块以增强多尺度上下文表示。此外，引入窗口化分布感知KL散度损失用于多解码阶段的深度监督。在多种医学图像分割基准上的广泛实验表明，该方法在保持中等模型复杂度的同时，实现了最先进的性能和强大的泛化能力。
英文摘要：
Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approaches remain task-specific, showing strong performance on individual datasets but limited generalization across diverse imaging modalities. Moreover, many methods focus primarily on the encoder, relying on large pretrained backbones that increase computational complexity. In this paper, we propose a decoder-centric approach for generalized 2D medical image segmentation. The proposed Deco-Mamba follows a U-Net-like structure with a Transformer-CNN-Mamba design. The encoder combines a CNN block and Transformer backbone for efficient feature extraction, while the decoder integrates our novel Co-Attention Gate (CAG), Vision State Space Module (VSSM), and deformable convolutional refinement block to enhance multi-scale contextual representation. Additionally, a windowed distribution-aware KL-divergence loss is introduced for deep supervision across multiple decoding stages. Extensive experiments on diverse medical image segmentation benchmarks yield state-of-the-art performance and strong generalization capability while maintaining moderate model complexity. The source code will be released upon acceptance.
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。
