Search Results for author:
Found 12 papers, 0 papers with code
Recognizing Abnormal Heart Sounds Using Deep Learning
The work presented here applies deep learning to the task of automated cardiac auscultation, i. e. recognizing abnormalities in heart sounds.
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit
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.
Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings
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).
Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
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.
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals
Using the Physionet/CinC Challenge dataset, an 80-20% subject-level split was performed to create in-house training and test sets, respectively.
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss
We present a fully convolutional neural network for segmenting ischemic stroke lesions in CT perfusion images for the ISLES 2018 challenge.
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation
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
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
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.
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings.
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
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.
