AIMC Topic: Neural Networks, Computer

Clear Filters Showing 11791 to 11800 of 31376 articles

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), acce...

Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation.

Medical engineering & physics
BACKGROUND: To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermato...

A novel Lyapunov stability analysis of neutral-type Cohen-Grossberg neural networks with multiple delays.

Neural networks : the official journal of the International Neural Network Society
The major target of this research article is to conduct a new Lyapunov stability analysis of a special model of Cohen-Grossberg neural networks that include multiple delay terms in state variables of systems neurons and multiple delay terms in time d...

Printability for additive manufacturing with machine learning: Hybrid intelligent Gaussian process surrogate-based neural network model for Co-Cr alloy.

Journal of the mechanical behavior of biomedical materials
AM has revolutionized the manufacturing industry, involving several operating parameters that may affect the properties of the final manufactured part. In AM, LPBF has proved its reliability in producing dense components; however, process development...

Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

IEEE transactions on medical imaging
Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improved by recent deep learning based methods. These methods rely on large-scale well-annotated training d...

Harmonizing Pathological and Normal Pixels for Pseudo-Healthy Synthesis.

IEEE transactions on medical imaging
Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches based on the Generative Adversarial Network (GAN) have achieved promising ...

Unsupervised Histological Image Registration Using Structural Feature Guided Convolutional Neural Network.

IEEE transactions on medical imaging
Registration of multiple stained images is a fundamental task in histological image analysis. In supervised methods, obtaining ground-truth data with known correspondences is laborious and time-consuming. Thus, unsupervised methods are expected. Unsu...

Deformation-Compensated Learning for Image Reconstruction Without Ground Truth.

IEEE transactions on medical imaging
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has shown the potential of using multiple noisy measurements of the same obje...

Global and Local Feature Reconstruction for Medical Image Segmentation.

IEEE transactions on medical imaging
Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate th...

Deep Relation Learning for Regression and Its Application to Brain Age Estimation.

IEEE transactions on medical imaging
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn diffe...