MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation
This addresses the need for efficient segmentation models in resource-constrained clinical settings, though it appears incremental as it builds on existing U-Net and transformer architectures.
The paper tackles the problem of high computational cost in medical image segmentation models by proposing MFEnNet, which incorporates MetaFormer with pooling transformer blocks in a U-Net backbone, achieving competitive accuracy while significantly reducing computational cost compared to state-of-the-art models.
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 https://github.com/tranleanh/mfennet.