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Found 6 papers, 1 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.
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.
Simplified Transfer Learning for Chest Radiography Models Using Less Data
Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.
Predicting Poverty Level from Satellite Imagery using Deep Neural Networks
Determining the poverty levels of various regions throughout the world is crucial in identifying interventions for poverty reduction initiatives and directing resources fairly.
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Tuberculosis (TB) is a top-10 cause of death worldwide.
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States.
