Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
TL;DR
A novel framework enhances respiratory sound classification by aligning self-supervised encoders with medical terminology, achieving a 61.3% mean zero-shot AUC. This approach outperforms existing models while using significantly less data.
✦ Why It Matters
Consider implementing this framework to improve respiratory sound classification in your clinical applications today.
Key Takeaways
- Explore using LLMs to generate structured reports from metadata.
- Implement contrastive learning techniques to enhance model performance.
- Utilize less data while achieving higher accuracy in diagnostics.
- Align audio encoders with medical terminology for better zero-shot inference.
How It Works
The framework aligns self-supervised respiratory sound encoders with medical terminology by creating a shared latent space. It uses a medical LLM to synthesize structured reports from metadata, which serve as semantic anchors for contrastive learning.
The training combines a sigmoid-based contrastive loss with the encoder's native self-supervised learning objective, enhancing the model's ability to distinguish between different pathologies.