AIMC Topic: Neural Networks, Computer

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The Use of Artificial Neural Networks to Determine In-Hospital Mortality After Coronary Artery Bypass Surgery.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVES: The aim of this study was to present an artificial neural network (ANN) model for the accurate estimation of in-hospital mortality and to demonstrate the validity of the model with real data and a comparison with conventional multiple lin...

Neurophysiological brain mapping of human sleep-wake states.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to inclu...

Synchronization in finite time for variable-order fractional complex dynamic networks with multi-weights and discontinuous nodes based on sliding mode control strategy.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the global synchronization in finite time for variable-order fractional complex dynamic networks with multi-weights, where the dynamic nodes are modeled to be discontinuous, and subject to the local Hölder nonlinear growt...

Echocardiographic image multi-structure segmentation using Cardiac-SegNet.

Medical physics
PURPOSE: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise o...

An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstruction.

Medical physics
PURPOSE: Iterative convolutional neural networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, beca...

Text-Guided Neural Network Training for Image Recognition in Natural Scenes and Medicine.

IEEE transactions on pattern analysis and machine intelligence
Convolutional neural networks (CNNs) are widely recognized as the foundation for machine vision systems. The conventional rule of teaching CNNs to understand images requires training images with human annotated labels, without any additional instruct...

Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction.

IEEE transactions on medical imaging
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent sp...

Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning.

IEEE transactions on medical imaging
Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the de...

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.

IEEE transactions on medical imaging
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastogr...

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI.

IEEE transactions on medical imaging
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automati...