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• The proposed semi-supervised framework achieves performance comparable to fully supervised models on breast ultrasound segmentation with only 2.5% labeled data.result 90%
• The method significantly outperforms existing semi-supervised learning approaches for breast ultrasound segmentation.result 85%
• The framework uses training-free pseudo-label generation that leverages simple appearance-based descriptions (e.g., dark oval) for cross-domain structural transfer.method 90%
• The approach uses uncertainty entropy weighted fusion and adaptive uncertainty-guided reverse contrastive learning to improve boundary discrimination.technical 90%
• The method generates structurally consistent pseudo labels from vision-language models for breast ultrasound images.method 85%
• The paradigm is readily extensible to other imaging modalities or diseases with only a global appearance description required.technical 80%
• Existing vision-language models have limited effectiveness on breast ultrasound images because domain-specific prompts are difficult to transfer.limitation 85%
• Traditional semi-supervised learning suffers from unstable pseudo labels under extremely limited annotations for breast ultrasound segmentation.limitation 85%

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