29 citations · 55 across the 7 of their papers we have counts for
7 papers
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang +12
Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples…
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
Hari Prasanna Das, Ryan Tran, Japjot Singh +4
At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine L…
CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks
Robert Avram, Jeffrey E. Olgin, Alvin Wan +8
Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for dia…
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
Bryan Gopal, Ryan W. Han, Gautham Raghupathi +3
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation qu…
Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification
J. Weston Hughes, Taylor Sittler, Anthony D. Joseph +3
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40,000 ECGs, with…
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
Geoffrey H. Tison, Jeffrey Zhang, Francesca N. Delling +1
The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We…
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Brandon Ballinger, Johnson Hsieh, Avesh Singh +8
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple…