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Deep Learning - AI Medical Compendium

AIMC Topic: Deep Learning

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A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection.

International journal of neural systems Apr 11, 2018
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of t...
Nerve Net Algorithms Adult Deep Learning Cerebral Cortex Connectome Electroencephalography Signal Processing, Computer-Assisted Humans
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A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

Ophthalmology Apr 10, 2018
PURPOSE: Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these...
Middle Aged Severity of Illness Index Deep Learning Aged Cross-Sectional Studies Male Macula Lutea Photography Fundus Oculi Macular Degeneration Diagnostic Techniques, Ophthalmological Humans Algorithms Female Reproducibility of Results
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Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.

Public health nutrition Apr 6, 2018
OBJECTIVE: The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis.
Humans Image Processing, Computer-Assisted Natural Language Processing Food Preferences Diet Records Algorithms Nutrition Assessment Deep Learning
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MR fingerprinting Deep RecOnstruction NEtwork (DRONE).

Magnetic resonance in medicine Apr 6, 2018
PURPOSE: Demonstrate a novel fast method for reconstruction of multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods.
Humans Male Phantoms, Imaging Neuroimaging Normal Distribution Signal-To-Noise Ratio Deep Learning Models, Theoretical Computer Simulation Monte Carlo Method Image Processing, Computer-Assisted Reproducibility of Results Magnetic Resonance Imaging Neural Networks, Computer Adult Healthy Volunteers Algorithms Brain
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Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 4, 2018
The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features ...
Transcriptome Humans Diagnosis, Computer-Assisted Area Under Curve Neural Networks, Computer Algorithms Genomics Gene Expression Regulation Linear Models Deep Learning Neoplasms Software Computational Biology Pattern Recognition, Automated Gene Expression Profiling Models, Statistical
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Deformable Image Registration Using a Cue-Aware Deep Regression Network.

IEEE transactions on bio-medical engineering Apr 4, 2018
SIGNIFICANCE: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature.
Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Databases, Factual Nonlinear Dynamics Humans Brain Tomography, X-Ray Computed Algorithms
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Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

International journal of neural systems Apr 2, 2018
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not ...
Electroencephalography Seizures Algorithms Infant, Newborn Deep Learning Humans Neural Networks, Computer
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Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks.

IEEE transactions on bio-medical engineering Apr 2, 2018
OBJECTIVE: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address ...
Humans Algorithms Magnetic Resonance Imaging Brain Artifacts Deep Learning Image Processing, Computer-Assisted
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Deep Learning in Radiology.

Academic radiology Mar 30, 2018
As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, ...
Radiology Humans Deep Learning Image Processing, Computer-Assisted
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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

The AAPS journal Mar 30, 2018
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional...
Drug Discovery Artificial Intelligence Drug Design Deep Learning Big Data
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