Papers by Shakhrul Iman Siam

1 papers
RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification (2026.acl-long)

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Challenge: Existing models for respiratory diseases rely on audio inputs, but they lack generalizability and diagnostic precision.
Approach: They propose a multimodal foundation model that integrates respiratory sounds with medical history and symptoms to enhance diagnostic accuracy and disease detection capabilities.
Outcome: The proposed model improves AUROC and zero-shot tasks across five respiratory diseases using real-world datasets.

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