AIMC Topic: Neural Networks, Computer

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Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning.

Medical image analysis
Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject t...

Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks.

Computers in biology and medicine
Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-...

Ensemble of neural networks for 3D position estimation in monolithic PET detectors.

Physics in medicine and biology
We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality...

Adaptive latent similarity learning for multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Most existing clustering methods employ the original multi-view data as input to learn the similarity matrix which characterizes the underlying cluster structure shared by multiple views. This reduces the flexibility of multi-view clustering methods ...

Episodic memory: A hierarchy of spatiotemporal concepts.

Neural networks : the official journal of the International Neural Network Society
We propose a new model of episodic memory. It consists of a hierarchy of partial sequences of events, blended for consistency across space and time by feedforward/feedback links to concepts expressing their shared information. This blended concept hi...

Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure.

Journal of medical imaging and radiation sciences
BACKGROUND: Our previous work with iodine meta-iodobenzylguanidine (I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitations associated with the prognostic power of global parameters derived from planar imaging [1]. Employ...

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Journal of healthcare engineering
Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dom...

Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer-aided detection metho...

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Nanotechnology
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than tradit...

Sleep stage classification from heart-rate variability using long short-term memory neural networks.

Scientific reports
Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however ar...