Computer methods and programs in biomedicine
Dec 20, 2019
BACKGROUND AND OBJECTIVE: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantif...
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...
During speech listening, the brain could use contextual predictions to optimize sensory sampling and processing. We asked if such predictive processing is organized dynamically into separate oscillatory timescales. We trained a neural network that us...
Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the leading causes of cancer-related deaths on a global scale. Early detection and prevention of colorectal cancer is primarily enabled through manual screen...
PURPOSE: To apply deep convolution neural network to the segmentation task in myocardial arterial spin labeled perfusion imaging and to develop methods that measure uncertainty and that adapt the convolution neural network model to a specific false-p...
International journal of environmental research and public health
Oct 25, 2019
People explosion and fast economic growth are bringing a more serious land resource shortage crisis. Rational land-use allocation can effectively reduce this burden. Existing land-use allocation models may deal with a lot of challenges of land-use pl...
Neural networks : the official journal of the International Neural Network Society
Oct 15, 2019
The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by n...
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...
Neural networks : the official journal of the International Neural Network Society
Sep 17, 2019
This paper solves the event-triggered passivity and synchronization problems for delayed multiple-weighted coupled reaction-diffusion neural networks (DMWCRDNNs) composed of non-identical nodes with and without parameter uncertainties. On one side, b...
Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particularly on metrics focused on large pathologies. For diseases such as Multiple Sclerosis (MS), however,...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.