Search Results for author:
Found 7 papers, 3 papers with code
HeAR -- Health Acoustic Representations
Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community.
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.
Identifying Spurious Correlations using Counterfactual Alignment
We propose the counterfactual alignment method to detect and explore spurious correlations of black box classifiers.
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
A crucial aspect of optimizing Slowfast NFNet for this application lies in identifying effective audio augmentations.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography
Extracting quantitative body composition measures manually from CT scans is a cumbersome and time-consuming task.
