Search Results for author: Jonathan Rubin

Found 12 papers, 0 papers with code

Convolution-Free Waveform Transformers for Multi-Lead ECG Classification

no code implementations29 Sep 2021 Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu, Jonathan Rubin

We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings.

Classification ECG Classification

Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series

no code implementations15 Sep 2021 Asif Rahman, Yale Chang, Jonathan Rubin

Importantly, the hidden state activations represent feature coefficients that correlate with the prediction target and can be visualized as risk curves that capture the global relationship between individual input features and the outcome.

Time Series Time Series Analysis

CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation

no code implementations30 Apr 2019 Jonathan Rubin, S. Mazdak Abulnaga

We evaluate the results both qualitatively by visual comparison of generated MR to ground truth, as well as quantitatively by training fully convolutional neural networks that make use of generated MR data inputs to perform ischemic stroke lesion segmentation.

Generative Adversarial Network Image-to-Image Translation +3

Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images

no code implementations27 Feb 2019 Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, Polina Golland

We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.

BIG-bench Machine Learning

Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss

no code implementations2 Nov 2018 S. Mazdak Abulnaga, Jonathan Rubin

We present a fully convolutional neural network for segmenting ischemic stroke lesions in CT perfusion images for the ISLES 2018 challenge.

Ischemic Stroke Lesion Segmentation Lesion Segmentation +1

Automatic Detection of Arousals during Sleep using Multiple Physiological Signals

no code implementations5 Oct 2018 Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal

Using the Physionet/CinC Challenge dataset, an 80-20% subject-level split was performed to create in-house training and test sets, respectively.


Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

no code implementations20 Apr 2018 Jonathan Rubin, Deepan Sanghavi, Claire Zhao, Kathy Lee, Ashequl Qadir, Minnan Xu-Wilson

The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset consisting of 473, 064 chest x-rays and 206, 574 radiology reports collected from 63, 478 patients.

Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings

no code implementations10 Oct 2017 Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh

The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 seconds).

An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit

no code implementations16 Jul 2017 Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, David Moromisato

Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit.


Recognizing Abnormal Heart Sounds Using Deep Learning

no code implementations14 Jul 2017 Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan

The work presented here applies deep learning to the task of automated cardiac auscultation, i. e. recognizing abnormalities in heart sounds.

Sound Classification Specificity

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