AIMC Topic: Neural Networks, Computer

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Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information.

Asian Pacific journal of cancer prevention : APJCP Aug 1, 2020
PURPOSE: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.
Data Interpretation, Statistical ROC Curve United States Follow-Up Studies Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged Incidence Humans Neural Networks, Computer Machine Learning Prognosis Female
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Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.

Cancer imaging : the official publication of the International Cancer Imaging Society Aug 1, 2020
BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a chal...
Humans Neural Networks, Computer Imaging, Three-Dimensional Image Processing, Computer-Assisted Tomography, X-Ray Computed Multiple Pulmonary Nodules Deep Learning ROC Curve
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Research and Verification of Convolutional Neural Network Lightweight in BCI.

Computational and mathematical methods in medicine Aug 1, 2020
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convo...
Electroencephalography Databases, Factual Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Stochastic Processes Deep Learning Facial Expression Neural Networks, Computer Data Compression Models, Neurological Algorithms
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A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs.

Computational and mathematical methods in medicine Aug 1, 2020
Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing. However, the prediction performances of encoding models will have differe...
Algorithms Photic Stimulation Humans Databases, Factual Image Processing, Computer-Assisted Functional Neuroimaging Neural Networks, Computer Visual Perception Magnetic Resonance Imaging Artificial Intelligence Visual Cortex Models, Neurological
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An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

Computational and mathematical methods in medicine Aug 1, 2020
The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals....
Cluster Analysis Unsupervised Machine Learning Electroencephalography Epilepsy Seizures Signal Processing, Computer-Assisted Brain Humans Diagnosis, Computer-Assisted Deep Learning Neural Networks, Computer Algorithms
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Generative Adversarial Network Technologies and Applications in Computer Vision.

Computational intelligence and neuroscience Aug 1, 2020
Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to tradit...
Vision, Ocular Image Processing, Computer-Assisted Biomimetics Humans Facial Expression Neural Networks, Computer Computers
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Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations.

Journal of chemical information and modeling Jul 31, 2020
Recently, machine learning has been successfully applied to the prediction of nuclear magnetic resonance (NMR) chemical shifts. To build a prediction model, the existing methods require a training data set that comprises molecules whose NMR-active at...
Neural Networks, Computer Magnetic Resonance Imaging Magnetic Resonance Spectroscopy
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A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

Molecules (Basel, Switzerland) Jul 31, 2020
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas th...
Steel Construction Industry Weight-Bearing Engineering Neural Networks, Computer Databases, Factual Machine Learning Models, Theoretical Construction Materials
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A robust convolutional neural network for lung nodule detection in the presence of foreign bodies.

Scientific reports Jul 31, 2020
Lung cancer is a major cause of death worldwide. As early detection can improve outcome, regular screening is of great interest, especially for certain risk groups. Besides low-dose computed tomography, chest X-ray is a potential option for screening...
Tomography, X-Ray Computed Lung Solitary Pulmonary Nodule False Positive Reactions Foreign Bodies Humans Neural Networks, Computer
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Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

Journal of medical Internet research Jul 31, 2020
BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors an...
Risk Factors Asthma Male Neural Networks, Computer Retrospective Studies Female Deep Learning Risk Assessment Quality of Life Disease Progression Humans
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