6 papers
Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio
Ran Piao, Tsai-Ning Wang, Martijn den Dekker +4
Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We propose Federated Self-Contextu…
Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding
Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour +1
Pre-trained audio models excel at detecting acoustic patterns in auscultation sounds but often fail to grasp their clinical significance, limiting their use and performance in diag…
Adaptive Test-Time Scaling for Zero-Shot Respiratory Audio Classification
Tsai-Ning Wang, Herman Teun den Dekker, Lin-Lin Chen +2
Automated respiratory audio analysis promises scalable, non-invasive disease screening, yet progress is limited by scarce labeled data and costly expert annotation. Zero-shot infer…
Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings
Fan Wu, Tsai-Ning Wang, Nicolas Zumarraga +8
Auscultation is a vital diagnostic tool, yet its utility is often limited by subjective interpretation. While general-purpose Audio-Language Models (ALMs) excel in general domains,…
StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks
Yishan Wang, Tsai-Ning Wang, Mathias Funk +1
Listening to heart and lung sounds - auscultation - is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years…
CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour +1
Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on h…