AIMC Topic: Neural Networks, Computer

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Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment.

Journal of environmental management
Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The pres...

Vulnerability of classifiers to evolutionary generated adversarial examples.

Neural networks : the official journal of the International Neural Network Society
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine l...

AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.

Annals of nuclear medicine
OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this stud...

Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques.

Computers in biology and medicine
BACKGROUND: The purpose of this study was to develop and evaluate an algorithm for bone segmentation on whole-body CT using a convolutional neural network (CNN).

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.

Sensors (Basel, Switzerland)
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse trans...

A double-hit of stress and low-grade inflammation on functional brain network mediates posttraumatic stress symptoms.

Nature communications
Growing evidence indicates a reciprocal relationship between low-grade systemic inflammation and stress exposure towards increased vulnerability to neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, the neural correl...

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data.

Radiation oncology (London, England)
INTRODUCTION: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of dee...

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Biointerphases
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and e...

4D-CT deformable image registration using multiscale unsupervised deep learning.

Physics in medicine and biology
Deformable image registration (DIR) of 4D-CT images is important in multiple radiation therapy applications including motion tracking of soft tissue or fiducial markers, target definition, image fusion, dose accumulation and treatment response evalua...

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples.

International journal of cardiology
BACKGROUND: Deep learning (DL) has shown promising results in improving atrial fibrillation (AF) detection algorithms. However, these models are often criticized because of their "black box" nature.