Zou Le, Bu Xiangxu, Jiang Fengling, Wu Zhize, Sun Lingma, Dashtipour Kia, Gogate Mandar, Hussain Amir, Wang Xiaofeng
IEEE J Biomed Health Inform. 2025 Jun 25;PP. doi: 10.1109/JBHI.2025.3583108.
Computational resources, which presents a significant challenge in resourceconstrained environments, particularly in developing countries. Consequently, the development of decoding mechanisms that are both computationally efficient and lightweight is imperative. However, the performance of medical image segmentation is frequently limited by the simplicity of decoder designs. Balancing the optimization of decoder architectures with the reduction of computational demands while maintaining high model accuracy remains a formidable challenge. In this context, we introduce a novel decoder that integrates line deformable convolution and multi-scale self-attention (LDMSD). The multi-scale self-attention enhancement module within LDMSD leverages two distinct multi-scale self-attention mechanisms, thereby substantially improving the representational capacity of the feature maps. Furthermore, the decoder incorporates a linear deformable convolution attention-guided mechanism to augment the feature maps derived from skip connections. This mechanism effectively mitigates the inherent limitations of conventional convolution and enhances the model's ability to capture complex semantic relationships within the feature maps. Through this collaborative mechanism, LDMSD is able to capture target information from both global and multiscale perspectives, accurately locate the target's boundaries and structures, while maintaining its lightweight nature. Experimental results demonstrate that LDMSD outperforms the state-of-the-art decoders in terms of performance metrics, achieving a reduction in Floating Point Operations (FLOPs) by 77.36% and in parameter count by 81.66% when compared to the Cascaded Attention Decoder (CASCADE). To substantiate the efficacy of the proposed method, extensive experiments are conducted on six publicly available datasets. The results validate that the proposed method surpasses existing approaches in medical image segmentation tasks, both in terms of accuracy and computational efficiency.
计算资源在资源受限的环境中，尤其是在发展中国家，构成了重大挑战。因此，开发计算效率高且轻量级的解码机制势在必行。然而，医学图像分割的性能常常受到解码器设计简单性的限制。在保持高模型精度的同时，平衡解码器架构的优化与计算需求的降低仍然是一项艰巨的挑战。在此背景下，我们引入了一种新颖的解码器，它集成了线性可变形卷积和多尺度自注意力（LDMSD）。LDMSD中的多尺度自注意力增强模块利用了两种不同的多尺度自注意力机制，从而大幅提高了特征图的表征能力。此外，解码器还纳入了一种线性可变形卷积注意力引导机制，以增强从跳跃连接派生的特征图。这种机制有效地减轻了传统卷积的固有局限性，并增强了模型在特征图中捕捉复杂语义关系的能力。通过这种协作机制，LDMSD能够从全局和多尺度角度捕捉目标信息，准确地定位目标的边界和结构，同时保持其轻量级特性。实验结果表明，在性能指标方面，LDMSD优于当前最先进的解码器，与级联注意力解码器（CASCADE）相比，浮点运算量（FLOPs）减少了77.36%，参数数量减少了81.66%。为了证实所提方法的有效性，我们在六个公开可用的数据集上进行了广泛的实验。结果验证了所提方法在医学图像分割任务中，在准确性和计算效率方面均超越了现有方法。
