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Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
Not provided
zero-shot learningmedical AIcontrastive learningaudio classification
2609.00055
Builder Relevance
1h ago80%
Abstract
The paper proposes a framework that enhances self-supervised respiratory encoders by aligning them with medical terminology, enabling zero-shot inference in clinical diagnostics.
Reality Card
Core Claim
The proposed framework achieves a 61.3% mean zero-shot AUC, outperforming existing models while using only 43% of the data required by full-scale baselines.
Method / Result
Achieved a 61.3% mean zero-shot AUC across 9 tasks on 6 datasets.
Limitations
The reliance on synthesized structured reports from metadata may limit reproducibility due to potential variability in report quality.
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