AIMC Topic: Neural Networks, Computer

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Transferability of artificial neural networks for clinical document classification across hospitals: A case study on abnormality detection from radiology reports.

Journal of biomedical informatics
OBJECTIVE: Application of machine learning techniques for automatic and reliable classification of clinical documents have shown promising results. However, machine learning models require abundant training data specific to each target hospital and m...

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor.

Sensors (Basel, Switzerland)
This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for ...

Analysis and Simulation of Capacitor-Less ReRAM-Based Stochastic Neurons for the in-Memory Spiking Neural Network.

IEEE transactions on biomedical circuits and systems
The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM's conducting filament with built-in stochasticity is used to mimic the ...

CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.

Biomedical engineering online
BACKGROUND: Early and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for de...

Computed tomography super-resolution using deep convolutional neural network.

Physics in medicine and biology
The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolutio...

Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study.

Dento maxillo facial radiology
OBJECTIVES: To evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by ora...

Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks.

Artificial organs
Electromyogram (EMG) signal decoding is the essential part of myoelectric control. However, traditional machine learning methods lack the capability of learning and expressing the information contained in EMG signals, and the robustness of the myoele...

Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Primates, including humans, can typically recognize objects in visual images at a glance despite naturally occurring identity-preserving image transformations (e.g., changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechan...

miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

PLoS computational biology
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...

A novel fused convolutional neural network for biomedical image classification.

Medical & biological engineering & computing
With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis te...