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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.
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of chest X-ray tasks.
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort.
Learning to Detect Touches on Cluttered Tables
We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables.
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.
Supervised Transfer Learning at Scale for Medical Imaging
However, for medical imaging, the value of transfer learning is less clear.
