AIMC Topic: Neural Networks, Computer

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A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical ...

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning.

Journal of medical systems
Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra crani...

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

Physics in medicine and biology
A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, call...

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.

Journal of neural engineering
OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Arti...

Optimized fast GPU implementation of robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction.

PloS one
BACKGROUND: Robust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural n...

Induced-Current Learning Method for Nonlinear Reconstructions in Electrical Impedance Tomography.

IEEE transactions on medical imaging
Electrical impedance tomography (EIT) is an attractive technique that aims to reconstruct the unknown electrical property in a domain from the surface electrical measurements. In this work, the induced-current learning method (ICLM) is proposed to so...

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...

Learning Cascade Attention for fine-grained image classification.

Neural networks : the official journal of the International Neural Network Society
Fine-grained image classification is a challenging task due to the large inter-class difference and small intra-class difference. In this paper, we propose a novel Cascade Attention Model using the Deep Convolutional Neural Network to address this pr...

A consensus algorithm based on collective neurodynamic system for distributed optimization with linear and bound constraints.

Neural networks : the official journal of the International Neural Network Society
In this paper, an algorithm based on collective neurodynamic system is investigated for distributed constrained convex optimization, whose objective function is a sum of smooth convex functions and non-smooth L-norm functions. Inspired by recent adva...

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...