AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.
Uni- clip: Unified framework for contrastive language-image pre-training,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SD 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding
AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.