AIMC Topic: Neural Networks, Computer

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Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Medical image analysis
Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, ...

An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.

Marine pollution bulletin
Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial c...

Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
OBJECTIVES: To automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed and the reliability and accuracy were evaluated.

Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.

International journal of medical informatics
INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidn...

Accelerating cardiovascular model building with convolutional neural networks.

Medical & biological engineering & computing
The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional n...

Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables.

International journal of legal medicine
The performance of seven classification methods, binary logistic (BLR), probit (PR) and cumulative probit (CPR) regression, linear (LDA) and quadratic (QDA) discriminant analysis, artificial neural networks (ANN), and naïve Bayes classification (NBC)...

Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms.

Journal of neurointerventional surgery
BACKGROUND: Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a l...

Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

Endoscopy
BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly a...

Multiclass heterogeneous domain adaptation via bidirectional ECOC projection.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous domain adaptation aims to exploit the source domain data to train a prediction model for the target domain with different input feature space. Current methods either map the data points from different domains with different feature spac...