IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 20, 2016
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical ...
IEEE transactions on biomedical circuits and systems
Sep 16, 2016
In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem. A convolutional neural network and...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 30, 2016
Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important probl...
International journal of computer assisted radiology and surgery
Aug 30, 2016
PURPOSE: For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifie...
Suppression of bony structures in chest radiographs (CXRs) is potentially useful for radiologists and computer-aided diagnostic schemes. In this paper, we present an effective deep learning method for bone suppression in single conventional CXR using...
Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that...
Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural la...
BMC medical informatics and decision making
Jul 21, 2016
BACKGROUND: Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task f...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 19, 2016
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, an...
BACKGROUND: Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standard...
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