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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2601.00922 (eess)
[Submitted on 1 Jan 2026]

Title:MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation

Authors:Le-Anh Tran, Chung Nguyen Tran, Nhan Cach Dang, Anh Le Van Quoc, Jordi Carrabina, David Castells-Rufas, Minh Son Nguyen
View a PDF of the paper titled MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation, by Le-Anh Tran and Chung Nguyen Tran and Nhan Cach Dang and Anh Le Van Quoc and Jordi Carrabina and David Castells-Rufas and Minh Son Nguyen
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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.
Comments: 10 pages, 5 figures, MCT4SD 2025
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2601.00922 [eess.IV]
  (or arXiv:2601.00922v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2601.00922
arXiv-issued DOI via DataCite

Submission history

From: Le-Anh Tran PhD [view email]
[v1] Thu, 1 Jan 2026 13:45:50 UTC (4,093 KB)
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