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October 20, 2025Open Access

TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation

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Authors

MWMengzhu WangJLJiao LiSWShanshan Wang

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Overview

Innovative framework enhances medical image segmentation results by leveraging transferable semantics, suggesting new methodologies for SSL.

Key Points

  • TransMedSeg significantly improves medical image segmentation performance with better semantic alignment.
  • The framework uses a Transferable Semantic Augmentation module to enhance feature representations effectively.
  • Experiments show superior outcomes compared to existing semi-supervised methods across various medical image datasets.
  • The approach theoretically minimizes a loss function while maintaining low computational overhead.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68f58f68ece7a5b64f471306https://doi.org/10.48550/arxiv.2505.14753
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