AIMC Topic: Neural Networks, Computer

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A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.

Medical physics
PURPOSE: Convolutional neural network (CNN)-based image denoising techniques have shown promising results in low-dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel-level loss function. P...

Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.

Scientific reports
Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their ...

Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BMC bioinformatics
BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the ...

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.

IEEE transactions on medical imaging
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underly...

Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network.

IEEE journal of biomedical and health informatics
Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have ...

Data-Driven Texture Modeling and Rendering on Electrovibration Display.

IEEE transactions on haptics
With the introduction of variable friction displays, either based on ultrasonic or electrovibration technology, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic t...

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

International journal of medical informatics
BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set u...

Episodic Memory in Minicolumn Associative Knowledge Graphs.

IEEE transactions on neural networks and learning systems
A generalization of active neural associative knowledge graphs (ANAKGs) to their minicolumn form is presented in this paper. Each minicolumn represents a single symbol, and the activation of an individual neuron in a minicolumn depends on the context...

Increasing productivity of Spirulina platensis in photobioreactors using artificial neural network modeling.

Biotechnology and bioengineering
Although production of microalgae in open ponds is conventionally practiced due to its economy, exposure of the algae to uncontrollable elements impedes achievement of quality and it is desirable to develop closed reactor cultivation methods for the ...

Synthesis of CT images from digital body phantoms using CycleGAN.

International journal of computer assisted radiology and surgery
PURPOSE: The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer a...