AIMC Topic: Neural Networks, Computer

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A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

The American journal of pathology
Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based o...

A generative adaptive convolutional neural network with attention mechanism for driver fatigue detection with class-imbalanced and insufficient data.

Behavioural brain research
Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue detection systems. However, many existing methods involves tedious manual parameter tunings, a proces...

Convolutional neural networks reveal properties of reach-to-grasp encoding in posterior parietal cortex.

Computers in biology and medicine
Deep neural networks (DNNs) are widely adopted to decode motor states from both non-invasively and invasively recorded neural signals, e.g., for realizing brain-computer interfaces. However, the neurophysiological interpretation of how DNNs make the ...

A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

Journal of applied clinical medical physics
PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical ...

[The model transferability of AI in digital pathology : Potential and reality].

Pathologie (Heidelberg, Germany)
OBJECTIVE: Artificial intelligence (AI) holds the potential to make significant advancements in pathology. However, its actual implementation and certification for practical use are currently limited, often due to challenges related to model transfer...

Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis.

SAR and QSAR in environmental research
The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodolo...

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.

Scientific reports
Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical su...

A model-based direct inversion network (MDIN) for dual spectral computed tomography.

Physics in medicine and biology
. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Add...

Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Perceptron models have become integral tools for pattern recognition and classification problems in engineering fields. This study envisioned implementing artificial neural networks to forecast the performance of a mini-spray dryer for desulfurizatio...

Towards a better negative sampling strategy for dynamic graphs.

Neural networks : the official journal of the International Neural Network Society
As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a concomitant increase in the development of Temporal Graph Neural Networks (TGNNs). When training TGNN...