AIMC Topic: Neural Networks, Computer

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Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.

G3 (Bethesda, Md.)
The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of th...

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

NeuroImage. Clinical
Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron ...

Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

NeuroImage. Clinical
PURPOSE: Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International MS lesion challenges show a preference of convolutional neural networks (CNN) strategies, such as n...

A neural model of schemas and memory encoding.

Biological cybernetics
The ability to rapidly assimilate new information is essential for survival in a dynamic environment. This requires experiences to be encoded alongside the contextual schemas in which they occur. Tse et al. (Science 316(5821):76-82, 2007) showed that...

White blood cells detection and classification based on regional convolutional neural networks.

Medical hypotheses
White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Ba...

New approach to global Mittag-Leffler synchronization problem of fractional-order quaternion-valued BAM neural networks based on a new inequality.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel kind of neural networks named fractional-order quaternion-valued bidirectional associative memory neural networks (FQVBAMNNs) is formulated. On one hand, applying Hamilton rules in quaternion multiplication which is essentially...

Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs.

International journal of computer assisted radiology and surgery
PURPOSE: Convolutional neural networks (CNNs) have obtained enormous success in liver segmentation. However, there are several challenges, including low-contrast images, and large variations in the shape, and appearance of the liver. Incorporating pr...

Highly Efficient, Transparent, and Multifunctional Air Filters Using Self-Assembled 2D Nanoarchitectured Fibrous Networks.

ACS nano
Particulate matter (PM) pollution is a significant burden on global economies and public health. Most present air filters are heavy, bulky, and nontransparent and typically have inevitable compromise between removal efficiency and air permeability. W...

Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.

Computational and mathematical methods in medicine
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect...

Automated classification of histopathology images using transfer learning.

Artificial intelligence in medicine
Early and accurate diagnosis of diseases can often save lives. Diagnosis of diseases from tissue samples is done manually by pathologists. Diagnostics process is usually time consuming and expensive. Hence, automated analysis of tissue samples from h...