240 citations · 311 across the 9 of their papers we have counts for
9 papers
Automated Interpretable 2D Video Extraction from 3D Echocardiography
Milos Vukadinovic, Hirotaka Ieki, Yuki Sahashi +2
Although the heart has complex three-dimensional (3D) anatomy, conventional medical imaging with cardiac ultrasound relies on a series of 2D videos showing individual cardiac struc…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
David Choi, Milos Vukadinovic, Bryan He +3
Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and n…
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Milos Vukadinovic, Xiu Tang, Neal Yuan +5
Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocar…
Deep Learning Discovery of Demographic Biomarkers in Echocardiography
Grant Duffy, Shoa L. Clarke, Matthew Christensen +4
Deep learning has been shown to accurately assess 'hidden' phenotypes and predict biomarkers from medical imaging beyond traditional clinician interpretation of medical imaging. Gi…
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
David Ouyang, John Theurer, Nathan R. Stein +21
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiogr…
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Bryan He, Matthew Thomson, Meena Subramaniam +3
Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. This paper takes an imp…
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Grant Duffy, Paul P Cheng, Neal Yuan +15
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrop…
Minimizing Close-k Aggregate Loss Improves Classification
Bryan He, James Zou
In classification, the de facto method for aggregating individual losses is the average loss. When the actual metric of interest is 0-1 loss, it is common to minimize the average s…