# MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation Source: [MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-07T05:00:04.581Z Authors: Le-Anh Tran, Chung Nguyen Tran, Nhan Cach Dang, Anh Le Van Quoc, Jordi Carrabina, David Castells-Rufas, Minh Son Nguyen Published: 2026-01-01T13:45:50.000Z ## 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 https://github.com/tranleanh/mfennet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on CASIA Iris Interval (val)](): Semantic segmentation; CASIA Iris Interval (val); IoU: 94.84. - [Image Segmentation on ISIC 2016 (test)](): Image Segmentation; ISIC 2016 (test); Dice Coefficient: 89.13. ## Original sources and implementation [Original paper]() [PDF]()