AIMC Topic: Pattern Recognition, Automated
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Object class segmentation of RGB-D video using recurrent convolutional neural networks.
Neural networks : the official journal of the International Neural Network Society
Jan 30, 2017
Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations...
Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.
Sensors (Basel, Switzerland)
Jan 30, 2017
Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method t...
Biologically plausible learning in neural networks with modulatory feedback.
Neural networks : the official journal of the International Neural Network Society
Jan 28, 2017
Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new ...
Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.
Computers in biology and medicine
Jan 27, 2017
BACKGROUND: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is o...
Implementing Machine Learning in Radiology Practice and Research.
AJR. American journal of roentgenology
Jan 26, 2017
OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data ...
Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel.
Computers in biology and medicine
Jan 26, 2017
Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination...
Automated seizure detection using limited-channel EEG and non-linear dimension reduction.
Computers in biology and medicine
Jan 25, 2017
Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of bo...
Gap-free segmentation of vascular networks with automatic image processing pipeline.
Computers in biology and medicine
Jan 21, 2017
Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vasc...
Automatic detection and measurement of nuchal translucency.
Computers in biology and medicine
Jan 20, 2017
In this paper we propose a new methodology to support the physician both to identify automatically the nuchal region and to obtain a correct thickness measurement of the nuchal translucency. The thickness of the nuchal translucency is one of the main...
Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.
PloS one
Jan 19, 2017
In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictiona...
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