AIMC Topic: Neural Networks, Computer

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Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

European radiology
OBJECTIVES: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.

Automatic recognition of bladder tumours using deep learning technology and its clinical application.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Bladder cancer is a kind of tumors with a high recurrence rate. The improvement of the cure rate and prognosis of bladder tumor depends on the accurate recognition of bladder tumor under the cystoscope.

Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series: A case study on heart rates.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Physiological time series are common data sources in many health applications. Mining data from physiological time series is crucial for promoting healthy living and reducing governmental medical expenditure. Recently, resea...

Learning sparse and meaningful representations through embodiment.

Neural networks : the official journal of the International Neural Network Society
How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment? Here we investigate embodiment with a closed loop between action and perception as one key component in this p...

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

ACS nano
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for l...

ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The use of l (p = 1,2) norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent w...

Application of artificial neural networks to predict the COVID-19 outbreak.

Global health research and policy
BACKGROUND: Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers.

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype.

Computational biology and chemistry
Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, whi...

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation.

European radiology
OBJECTIVE: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we ...

Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision.

Neural networks : the official journal of the International Neural Network Society
Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpu...