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Uncertainty-Guided Prototype Reliability Enhancement Network for Few-Shot Medical Image Segmentation.

Junfei Hu, Tao Zhou, Kaiwen Huang

    IEEE Transactions on Medical Imaging
    |October 14, 2025
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    Summary

    This study introduces the Uncertainty-guided Prototype Reliability Enhancement Network (UPRE-Net) for few-shot medical image segmentation. The novel approach enhances prototype reliability and data utilization, significantly outperforming existing methods in medical segmentation tasks.

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    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Machine Learning

    Background:

    • Few-shot learning (FSL) is crucial for medical image segmentation due to limited labeled data.
    • Current FSL methods struggle with effective prototype learning for accurate segmentation.
    • Existing approaches often rely on basic nearest-neighbor searching for segmentation.

    Purpose of the Study:

    • To propose the Uncertainty-guided Prototype Reliability Enhancement Network (UPRE-Net) for few-shot medical image segmentation.
    • To improve the reliability and informativeness of class prototypes in data-scarce medical imaging.
    • To enhance the fusion of information from multiple support images for better segmentation outcomes.

    Main Methods:

    • Implemented a dual-support branch with augmentation for comprehensive information extraction from support images.
    • Introduced an Uncertainty-guided Prototype Generation (UPG) module to select informative global and local prototypes using uncertainty measures.
    • Developed a Reliable Dynamic Fusion (RDF) module for adaptive integration of dual-support branch predictions.
    • Utilized an Uncertainty-induced Weighted Loss (UWL) to focus model training on high-uncertainty regions.

    Main Results:

    • UPRE-Net demonstrated significant performance improvements over state-of-the-art methods on four benchmark medical image datasets.
    • The proposed UPG module effectively enhances prototype reliability by selecting informative prototypes.
    • The RDF module successfully integrates information from dual-support branches for more robust segmentation.
    • UWL loss function guided the model to prioritize uncertain regions, improving segmentation accuracy.

    Conclusions:

    • UPRE-Net offers a robust solution for few-shot medical image segmentation by enhancing prototype reliability and information fusion.
    • The uncertainty-guided approach effectively addresses the challenges of limited data in medical segmentation.
    • Experimental results validate the superiority of UPRE-Net, highlighting its potential for clinical applications.