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20242026
most citedUnified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice

1 citations · 1 across the 9 of their papers we have counts for

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11 papers · 1 filter

cs.LG2026

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

Hamza Shafiq, Hung Manh Pham, Bin Zhu +3

Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are…

cs.LG2026

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…

cs.LG2026

Learning ECG Image Representations via Dual Physiological-Aware Alignments

Hung Manh Pham, Jialu Tang, Aaqib Saeed +3

Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However,…

cs.LG2026

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…

cs.LG2026

UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR

Jialu Tang, Tong Xia, Yuan Lu +1

Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocardiogram (ECG). Large Language…

cs.LG2025

Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignment

Jialu Tang, Hung Manh Pham, Ignace De Lathauwer +4

Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unsee…