no code implementations • 14 Oct 2023 • Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W Gregory, Cynthia R Gregory, Meihua Zhu, David O Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited.
no code implementations • 29 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.
no code implementations • 15 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.
no code implementations • 30 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
no code implementations • 27 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.
no code implementations • 14 Nov 2018 • Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate.
no code implementations • 2 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.
no code implementations • 5 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.
no code implementations • 20 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.
no code implementations • 10 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).
no code implementations • 16 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.
no code implementations • 14 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.