RespiraMFM reports 9.15% AUROC gain in supervised fine-tuning and 20.98% in zero-shot settings over baselines by aligning respiratory audio with clinical text across seven real-world datasets for five diseases.
arXiv preprint arXiv:2203.16141 , year=
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RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification
RespiraMFM reports 9.15% AUROC gain in supervised fine-tuning and 20.98% in zero-shot settings over baselines by aligning respiratory audio with clinical text across seven real-world datasets for five diseases.