AIMC Topic: Algorithms

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Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.This retrospective single-center study included ncCT data of 15...

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Medicine and science in sports and exercise
PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices agains...

Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting.

Neural networks : the official journal of the International Neural Network Society
In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matr...

Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the optimal tracking issue for continuous-time (CT) nonlinear asymmetric constrained zero-sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking contr...

Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI.

Magnetic resonance in medicine
PURPOSE: We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior a...

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Methods (San Diego, Calif.)
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interaction...

MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images.

Medical image analysis
The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a f...

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

Pacing and clinical electrophysiology : PACE
BACKGROUND: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare d...

Workout Classification Using a Convolutional Neural Network in Ensemble Learning.

Sensors (Basel, Switzerland)
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizin...