DAUNet用可变形卷积和无参数注意力提升医学图像分割精度，模型轻量易部署。
DAUNet: A Lightweight UNet Variant with Deformable Convolutions and Parameter-Free Attention for Medical Image Segmentation
- 采用可变形卷积和无参数注意力机制，增强对形变和关键区域的感知能力。
- 在两个数据集上实现更高分割精度，且参数量显著低于现有模型。
- 适合实时、资源受限的临床场景，对低对比度和缺失上下文有强鲁棒性。
医学图像分割在自动化诊断与治疗规划中至关重要。本文提出DAUNet，一种新型轻量级UNet变体，融合可变形卷积V2与无参数注意力（SimAM），在不增加模型复杂度的前提下提升空间自适应性与上下文感知特征融合能力。其瓶颈模块使用动态可变形核处理几何变化，解码器与跳跃路径则通过SimAM注意力模块实现显著性感知优化。在两个挑战性数据集FH-PS-AoP（胎儿头颅与耻骨联合超声）和FUMPE（基于CT的肺栓塞检测）上的广泛评估表明，DAUNet在Dice分数、HD95和ASD指标上优于当前最优模型，同时保持优异的参数效率。消融实验验证了可变形卷积与SimAM注意力的独立贡献。该模型对缺失上下文和低对比区域表现出强鲁棒性，适用于实时与资源受限的临床环境。
原文摘要 · Abstract (English)
Medical image segmentation plays a pivotal role in automated diagnostic and treatment planning systems. In this work, we present DAUNet, a novel lightweight UNet variant that integrates Deformable V2 Convolutions and Parameter-Free Attention (SimAM) to improve spatial adaptability and context-aware feature fusion without increasing model complexity. DAUNet's bottleneck employs dynamic deformable kernels to handle geometric variations, while the decoder and skip pathways are enhanced using SimAM attention modules for saliency-aware refinement. Extensive evaluations on two challenging datasets, FH-PS-AoP (fetal head and pubic symphysis ultrasound) and FUMPE (CT-based pulmonary embolism detection), demonstrate that DAUNet outperforms state-of-the-art models in Dice score, HD95, and ASD, while maintaining superior parameter efficiency. Ablation studies highlight the individual contributions of deformable convolutions and SimAM attention. DAUNet's robustness to missing context and low-contrast regions establishes its suitability for deployment in real-time and resource-constrained clinical environments.
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