AIMC Topic: Neural Networks, Computer

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Boosting integral-based human pose estimation through implicit heatmap learning.

Neural networks : the official journal of the International Neural Network Society
Human pose estimation typically encompasses three categories: heatmap-, regression-, and integral-based methods. While integral-based methods possess advantages such as end-to-end learning, full-convolution learning, and being free from quantization ...

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...

A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental f...

A spatio-temporal graph convolutional network for ultrasound echocardiographic landmark detection.

Medical image analysis
Landmark detection is a crucial task in medical image analysis, with applications across various fields. However, current methods struggle to accurately locate landmarks in medical images with blurred tissue boundaries due to low image quality. In pa...

Towards multimodal graph neural networks for surgical instrument anticipation.

International journal of computer assisted radiology and surgery
PURPOSE: Decision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based in...

Proton spot dose estimation based on positron activity distributions with neural network.

Medical physics
BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently...

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

International journal of neural systems
The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook t...

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Journal of computational neuroscience
Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) ...

Application of triple-branch artificial neural network system for catalytic pellets combustion.

Journal of environmental management
On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to...