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This is a Quantitative Study study.

Joint Learning of Confidence Fusion, Semantic Alignment and Group-Guided Reliability: A Novel Semi-Supervised Learning Framework for 3D Medical Image Segmentation

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Observational analysis improved segmentation accuracy in CT scans, suggesting enhanced feature learning and reliability in semi-supervised learning.

Resulting in superior accuracy across diverse anatomical structures, the approach outperformed state-of-the-art methods.
Achieving improved generalization under limited annotation, the framework enhances the reliability of pseudo-labels.
Assessment using a unified semi-supervised learning framework with feature learning and structural modeling.
This method highlights the importance of addressing inter-class imbalance to achieve effective segmentation.

Zhou et al. (2025) studied this question.

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View Full Paper

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Authors

Observational analysis improved segmentation accuracy in CT scans, suggesting enhanced feature learning and reliability in semi-supervised learning.

Resulting in superior accuracy across diverse anatomical structures, the approach outperformed state-of-the-art methods.
Achieving improved generalization under limited annotation, the framework enhances the reliability of pseudo-labels.
Assessment using a unified semi-supervised learning framework with feature learning and structural modeling.
This method highlights the importance of addressing inter-class imbalance to achieve effective segmentation.

Zhou et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark