Recent advances in recurrent neural networks

H Salehinejad, S Sankar, J Barfett, E Colak… - arXiv preprint arXiv …, 2017 - arxiv.org
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies
from sequential and time-series data. The RNNs have a stack of non-linear units where …

Canadian Association of Radiologists white paper on artificial intelligence in radiology

…, W Guest, J Chong, J Barfett… - Canadian …, 2018 - journals.sagepub.com
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation
phase in many fields, including medicine. The combination of improved availability of large …

Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs

…, B Gray, K Perampaladas, J Barfett - Investigative …, 2017 - journals.lww.com
Objectives Convolutional neural networks (CNNs) are a subtype of artificial neural network
that have shown strong performance in computer vision tasks including image classification. …

Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks

…, S Valaee, T Dowdell, E Colak, J Barfett - … on acoustics, speech …, 2018 - ieeexplore.ieee.org
Medical datasets are often highly imbalanced with over-representation of common medical
problems and a paucity of data from rare conditions. We propose simulation of pathology in …

[HTML][HTML] Music intervention approaches for Alzheimer's disease: A review of the literature

…, L Fornazzari, TA Schweizer, J Barfett… - Frontiers in …, 2019 - frontiersin.org
Music interventions have been widely adopted as a potential non-pharmacological therapy
for patients with Alzheimer’s disease (AD) to treat cognitive and/or behavioral symptoms of …

Dynamic CT angiography and CT perfusion employing a 320-detector row CT

EJ Salomon, J Barfett, W Peter, A Willems… - Clinical …, 2009 - search.proquest.com
The aim of this study is to report the authors’ initial clinical experience of a 320-detector row
computed tomography (CT) scanner in cerebrovascular disorders. Volumetric CT using the …

Synthesizing chest X-ray pathology for training deep convolutional neural networks

…, E Colak, T Dowdell, J Barfett… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Medical datasets are often highly imbalanced with over-representation of prevalent
conditions and poor representation of rare medical conditions. Due to privacy concerns, it is …

Myocardial strain imaging by cardiac magnetic resonance for detection of subclinical myocardial dysfunction in breast cancer patients receiving trastuzumab and …

…, D Thavendiranathan, R Haq, JJ Barfett… - International Journal of …, 2018 - Elsevier
Background Our objectives were to evaluate the temporal changes in CMR-based strain
imaging, and examine their relationship with left ventricular ejection fraction (LVEF), in patients …

Image augmentation using radial transform for training deep neural networks

…, S Valaee, T Dowdell, J Barfett - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
Deep learning models have a large number of free parameters that must be estimated by
efficient training of the models on a large number of training data samples to increase their …

[HTML][HTML] Longitudinal assessment of right ventricular structure and function by cardiovascular magnetic resonance in breast cancer patients treated with trastuzumab: a …

…, KKW Chan, R Haq, A Kirpalani, JJ Barfett… - Journal of …, 2016 - Elsevier
Background There are limited data on the effects of trastuzumab on the right ventricle (RV).
Therefore, we sought to evaluate the temporal changes in right ventricular (RV) structure and …