提出一种上下文感知门控解码器，提升医学图像分割精度与效率
MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation
- 通过门控跳跃融合与多尺度通道重校准实现跨尺度特征对齐
- 在11个基准上均超越主流方法，边界保持更准确
- 适合需要高精度分割的临床医学图像分析任务
医学图像分割依赖编码器-解码器架构在低对比度、结构模糊和尺度变化等挑战下，将丰富特征表示转化为精确的像素级预测。尽管大规模预训练和基于Transformer的编码器显著提升了特征提取能力，但解码器设计仍限制分割精度，尤其在跨尺度对齐、上下文整合和边界保持方面。本文从解码器中心视角出发，提出一种上下文感知门控解码器，系统性地调控解码过程中的特征融合与上下文聚合。该解码器融合轻量级多尺度通道重校准、带有空间竞争的门控跳跃融合，以及将编码器全局信息注入中间解码阶段的全局上下文聚合机制，有效实现强预训练编码器表示到空间一致预测的转换。在11个医学图像分割基准上的大量实验验证了该方法的有效性，结果表明其持续优于多个强基线，同时保持计算实用性。
原文摘要 · Abstract (English)
Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation. In this work, we revisit medical image segmentation from a decoder-centric perspective and propose a context-aware gated decoder that systematically regulates feature fusion and contextual aggregation throughout the decoding process. The proposed decoder integrates lightweight multi-scale channel recalibration, gated skip fusion with spatial competition and a global context aggregation mechanism that injects encoder-wide information into intermediate decoding stages. This design enables effective translation of strong pretrained encoder representations into spatially consistent predictions. Extensive experiments across 11 medical image segmentation benchmarks validate the effectiveness and demonstrate that the proposed approach consistently outperforms strong baselines while remaining computationally practical. Code: https://github.com/saadwazir/MedCAGD
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