Search Results for author: Geoffrey H. Tison

Found 8 papers, 2 papers with code

BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors

1 code implementation17 Apr 2023 Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin, Pranav Rajpurkar

Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.

Self-Supervised Learning

CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks

no code implementations14 Jun 2021 Robert Avram, Jeffrey E. Olgin, Alvin Wan, Zeeshan Ahmed, Louis Verreault-Julien, Sean Abreau, Derek Wan, Joseph E. Gonzalez, Derek Y. So, Krishan Soni, Geoffrey H. Tison

Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms.

Management

3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations

no code implementations21 Apr 2021 Bryan Gopal, Ryan W. Han, Gautham Raghupathi, Andrew Y. Ng, Geoffrey H. Tison, Pranav Rajpurkar

We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram.

Contrastive Learning Time Series Analysis

Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification

no code implementations3 Dec 2018 J. Weston Hughes, Taylor Sittler, Anthony D. Joseph, Jeffrey E. Olgin, Joseph E. Gonzalez, Geoffrey H. Tison

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 labels derived from cardiologist clinical interpretations.

Classification General Classification

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

1 code implementation6 Jul 2018 Geoffrey H. Tison, Jeffrey Zhang, Francesca N. Delling, Rahul C. Deo

We identified 36, 186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease.

Binary Classification Decision Making

DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction

no code implementations7 Feb 2018 Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire, Jeffrey E. Olgin, Mark J. Pletcher

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 medical conditions, including diabetes (0. 8451), high cholesterol (0. 7441), high blood pressure (0. 8086), and sleep apnea (0. 8298).

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