Abstract
Semantic segmentation is crucial for medical image analysis, enabling precise disease diagnosis and treatment planning. However, many advanced models employ complex architectures, limiting their use in resource-constrained clinical settings. This paper proposes MFEnNet, an efficient medical image segmentation framework that incorporates MetaFormer in the encoding phase of the U-Net backbone. MetaFormer, an architectural abstraction of vision transformers, provides a versatile alternative to convolutional neural networks by transforming tokenized image patches into sequences for global context modeling. To mitigate the substantial computational cost associated with self-attention, the proposed framework replaces conventional transformer modules with pooling transformer blocks, thereby achieving effective global feature aggregation at reduced complexity. In addition, Swish activation is used to achieve smoother gradients and faster convergence, while spatial pyramid pooling is incorporated at the bottleneck to improve multi-scale feature extraction. Comprehensive experiments on different medical segmentation benchmarks demonstrate that the proposed MFEnNet approach attains competitive accuracy while significantly lowering computational cost compared to state-of-the-art models. The source code for this work is available at this https URL.
Abstract (translated)
语义分割在医学图像分析中至关重要，它能够实现精确的疾病诊断和治疗计划制定。然而，许多先进的模型采用了复杂的架构，这限制了它们在资源受限的临床环境中的应用。本文提出了一种名为MFEnNet的高效医疗影像分割框架，该框架将MetaFormer整合到了U-Net骨干网络的编码阶段中。MetaFormer是视觉变换器的一种结构抽象，它提供了一个灵活的选择，能够替代卷积神经网络，在对图像补丁进行标记化处理后形成序列以实现全局上下文建模。为了减轻与自注意力机制相关联的巨大计算成本，所提出的框架用池化变换模块替换了传统的变换模块，从而在降低复杂度的同时实现了有效的全局特征聚合。此外，采用Swish激活函数来获得更平滑的梯度和更快的收敛速度，并且在瓶颈层中融入空间金字塔池以提升多尺度特征提取能力。 通过不同的医学分割基准数据集进行的全面实验表明，提出的MFEnNet方法能够达到与最先进的模型相当的精度，同时大大降低了计算成本。这项工作的源代码可在此[URL]获取。
URL
https://arxiv.org/abs/2601.00922
