AIMC Topic: Neural Networks, Computer

Clear Filters Showing 18681 to 18690 of 31376 articles

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

PLoS computational biology
Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-dept...

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...

Cross-modality deep learning: Contouring of MRI data from annotated CT data only.

Medical physics
PURPOSE: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-segmentation algorithms for organs-at-risk and radiation targets. Current practice of manual segmentation is subjective and time-consuming. While deep l...

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters o...

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Neurobiology of aging
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learni...

Greedy auto-augmentation for n-shot learning using deep neural networks.

Neural networks : the official journal of the International Neural Network Society
The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmenta...

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the ...

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.

Molecules (Basel, Switzerland)
Permeation through the blood-brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence num...

A k-space-to-image reconstruction network for MRI using recurrent neural network.

Medical physics
PURPOSE: Reconstructing the images from undersampled k-space data are an ill-posed inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic resonance (MR) images directly from k-space data using a recurrent neural n...

Continuous Similarity Learning with Shared Neural Semantic Representation for Joint Event Detection and Evolution.

Computational intelligence and neuroscience
In the era of the rapid development of today's Internet, people often feel overwhelmed by vast official news streams or unofficial self-media tweets. To help people obtain the news topics they care about, there is a growing need for systems that can ...