AIMC Topic: Neural Networks, Computer

Clear Filters Showing 4671 to 4680 of 31376 articles

Real-time monitoring of single dendritic cell maturation using deep learning-assisted surface-enhanced Raman spectroscopy.

Theranostics
Dynamic real-time detection of dendritic cell (DC) maturation is pivotal for accurately predicting immune system activation, assessing vaccine efficacy, and determining the effectiveness of immunotherapy. The heterogeneity of cells underscores the s...

GraphPI: Efficient Protein Inference with Graph Neural Networks.

Journal of proteome research
The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled ...

Graph based recurrent network for context specific synthetic lethality prediction.

Science China. Life sciences
The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep ...

Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.

Statistics in medicine
An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure tim...

Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

Computers in biology and medicine
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a nov...

Open-set long-tailed recognition via orthogonal prototype learning and false rejection correction.

Neural networks : the official journal of the International Neural Network Society
Learning from data with long-tailed and open-ended distributions is highly challenging. In this work, we propose OLPR, which is a new dual-stream Open-set Long-tailed recognition framework based on orthogonal Prototype learning and false Rejection co...

Graph explicit pooling for graph-level representation learning.

Neural networks : the official journal of the International Neural Network Society
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discard...

Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture...

HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling.

Medical image analysis
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic se...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Talanta
Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vul...