AIMC Topic: Datasets as Topic

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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports Jul 13, 2018
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...
Male ROC Curve Data Interpretation, Statistical Models, Biological Female Young Adult Adult Rare Diseases Narcolepsy Polysomnography Sleep, REM Supervised Machine Learning Datasets as Topic Stochastic Processes Humans Databases, Factual Sleep Latency
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miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

PLoS computational biology Jul 13, 2018
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miR...
MicroRNAs Datasets as Topic Gene Expression Regulation Gene Targeting 3' Untranslated Regions Computer Simulation Humans Binding Sites Reproducibility of Results Deep Learning Neural Networks, Computer Thermodynamics RNA, Messenger
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Consistent Semantic Annotation of Outdoor Datasets via 2D/3D Label Transfer.

Sensors (Basel, Switzerland) Jul 12, 2018
The advance of scene understanding methods based on machine learning relies on the availability of large ground truth datasets, which are essential for their training and evaluation. Construction of such datasets with imagery from real sensor data ho...
Semantics Data Curation Machine Learning Robotics Datasets as Topic
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Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.

Japanese journal of radiology Jul 7, 2018
PURPOSE: To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.
Humans Neural Networks, Computer Magnetic Resonance Imaging Reference Values Algorithms Datasets as Topic Brain Signal-To-Noise Ratio Imaging, Three-Dimensional
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Classification of crystallization outcomes using deep convolutional neural networks.

PloS one Jun 20, 2018
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algori...
Datasets as Topic Neural Networks, Computer Crystallization Algorithms Crystallography, X-Ray Image Processing, Computer-Assisted
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Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin Jun 18, 2018
PURPOSE:  In forensic odontology the comparison between antemortem and postmortem panoramic radiographs (PRs) is a reliable method for person identification. The purpose of this study was to improve and automate identification of unknown people by co...
Datasets as Topic Child Image Processing, Computer-Assisted Humans Radiography, Panoramic Forensic Dentistry Aged Male Aged, 80 and over Middle Aged Young Adult Adolescent Adult Artificial Intelligence Algorithms Female
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Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology Jun 10, 2018
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation.
Humans Neural Networks, Computer Diagnosis, Differential Datasets as Topic Machine Learning Sensitivity and Specificity Radiography, Thoracic Cloud Computing
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Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

JCI insight Jun 7, 2018
We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator depen...
Imaging, Three-Dimensional Organ Size Placenta Pregnancy Pregnancy Trimester, First Female Datasets as Topic Ultrasonography, Prenatal Deep Learning Humans
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The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

NeuroImage Jun 2, 2018
Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accura...
Magnetic Resonance Imaging Sample Size Datasets as Topic Connectome Behavior Young Adult Adult Algorithms Male Cognition Brain Female Humans Machine Learning
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Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.

NeuroImage Apr 27, 2018
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brai...
Neuroimaging Humans Longitudinal Studies Neurodevelopmental Disorders Machine Learning Magnetic Resonance Imaging Child Development Brain Datasets as Topic Child, Preschool Female Cognition Male
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