FSC uses unsupervised clustering for pseudo-label episodes and a three-stage federated pipeline to achieve 71.6% accuracy in 2-way 2-shot in-context diagnosis of respiratory and cardiac audio conditions.
Careaqa: A cardiac and respiratory audio question answer- ing model for open-ended diagnostic reasoning,
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Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio
FSC uses unsupervised clustering for pseudo-label episodes and a three-stage federated pipeline to achieve 71.6% accuracy in 2-way 2-shot in-context diagnosis of respiratory and cardiac audio conditions.