Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. In particular, we investigated the applicability of training CNNs on a patient-by-patient basis for t...
Neural networks : the official journal of the International Neural Network Society
Sep 21, 2019
In this paper, the finite-time resilient H state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator ...
PURPOSE: Deep neural network-based image reconstruction has demonstrated promising performance in medical imaging for undersampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especial...
PURPOSE: Positron emission tomography (PET) is an important tool for nuclear medical imaging. It has been widely used in clinical diagnosis, scientific research, and drug testing. PET is a kind of emission computed tomography. Its basic imaging princ...
BACKGROUND AND AIM: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide death. Few empirical data are available regarding the temporal correlation between depression symptom...
OBJECTIVE: To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.
OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).
We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus...
Circulation. Cardiovascular quality and outcomes
Sep 5, 2019
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...
IEEE transactions on neural networks and learning systems
Sep 4, 2019
This article focuses on a problem important to automatic machine learning: the automatic processing of a nonpreprocessed time series. The convolutional neural network (CNN) is one of the most popular neural network (NN) algorithms for pattern recogni...