AIMC Topic: Neural Networks, Computer

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Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD), the leading cause of mortality, is caused by atherosclerotic plaque buildup in the arteries. The gold standard for the diagnosis of CAD is via X-ray coronary angiography (XCA) during percutaneo...

Cooperative GAN: Automated tympanic membrane anomaly detection using a Cooperative Observation Network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Recently, artificial intelligence (AI) has been applied to otolaryngology. However, existing supervised learning methods cannot easily predict data outside the learning domain. Moreover, collecting diverse medical data has ...

A Hierarchical Mixture-Of-Experts Framework for Few Labeled Node Classification.

Neural networks : the official journal of the International Neural Network Society
Few Labeled Node Classification (FLNC) is a challenging subtask of node classification, where training nodes are extremely limited, often with only one or two labels per class. While Graph Neural Networks (GNNs) show promise, they often suffer from f...

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

Journal of hazardous materials
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT images often lack observable vessels, we focus on segmenting hepatic vessels within preoperative MR i...

Neural-network-based practical specified-time resilient formation maneuver control for second-order nonlinear multi-robot systems under FDI attacks.

Neural networks : the official journal of the International Neural Network Society
This paper presents a specified-time resilient formation maneuver control approach for second-order nonlinear multi-robot systems under false data injection (FDI) attacks, incorporating an offline neural network. Building on existing works in integra...

Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralize...

Label as Equilibrium: A performance booster for Graph Neural Networks on node classification.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Network (GNN) is effective in graph mining and has become a dominant solution to the node classification task. Recently, a series of label reuse approaches emerged to boost the node classification performance of GNN. They repeatedly inpu...

GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation.

Neural networks : the official journal of the International Neural Network Society
Next Point-of-Interest (POI) recommendation is crucial in location-based applications, analyzing user behavior patterns from historical trajectories. Existing studies usually use graph structures and attention mechanisms for sequential prediction wit...

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the firs...