Search Results for author: Bryan He

Found 13 papers, 6 papers with code

Deep Learning Discovery of Demographic Biomarkers in Echocardiography

no code implementations13 Jul 2022 Grant Duffy, Shoa L. Clarke, Matthew Christensen, Bryan He, Neal Yuan, Susan Cheng, David Ouyang

When predicting race, we show that tuning the proportion of a confounding variable (sex) in the training data significantly impacts model AUC (ranging from 0. 57 to 0. 84), while in training a sex prediction model, tuning a confounder (race) did not substantially change AUC (0. 81 - 0. 83).

CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq

no code implementations13 Oct 2021 Bryan He, Matthew Thomson, Meena Subramaniam, Richard Perez, Chun Jimmie Ye, James Zou

Predicting phenotype from scRNA-seq is challenging for standard machine learning methods -- the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous.

Interpretable Machine Learning

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

1 code implementation23 Jun 2021 Grant Duffy, Paul P Cheng, Neal Yuan, Bryan He, Alan C. Kwan, Matthew J. Shun-Shin, Kevin M. Alexander, Joseph Ebinger, Matthew P. Lungren, Florian Rader, David H. Liang, Ingela Schnittger, Euan A. Ashley, James Y. Zou, Jignesh Patel, Ronald Witteles, Susan Cheng, David Ouyang

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis.

Video-based AI for beat-to-beat assessment of cardiac function

1 code implementation Nature 2020 David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curtis P. Langlotz, Paul A. Heidenreich, Robert A. Harrington, David H. Liang, Euan A. Ashley, James Y. Zou

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.

LV Segmentation

Minimizing Close-k Aggregate Loss Improves Classification

1 code implementation1 Nov 2018 Bryan He, James Zou

In classification, the de facto method for aggregating individual losses is the average loss.

Classification General Classification

Inferring Generative Model Structure with Static Analysis

no code implementations NeurIPS 2017 Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.

Accelerated Stochastic Power Iteration

2 code implementations10 Jul 2017 Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu

We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.

Dimensionality Reduction

Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data

no code implementations25 Oct 2016 Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré

Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set.

Relation Extraction

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

no code implementations NeurIPS 2016 Bryan He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions.

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