AIMC Topic: Neural Networks, Computer

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Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography.

European journal of radiology
PURPOSE: The purpose of this study was to evaluate the performance of a deep learning system for the automatic diagnosis and classification of rib fractures.

Two stream Non-Local CNN-LSTM network for the auxiliary assessment of mental retardation.

Computers in biology and medicine
At present, the assessment of mental retardation is mainly based on clinical interview, which requires the participation of experienced psychiatrist and is laborious. Studies have shown that there are correlations between mental retardation and abnor...

Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.

Biomolecules
Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has a...

A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm.

Sensors (Basel, Switzerland)
Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different ne...

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

BMC medical research methodology
BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical no...

Single-layer vision transformers for more accurate early exits with less overhead.

Neural networks : the official journal of the International Neural Network Society
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task that often relies on dynamic inference methods such as early exiting. In...

Self-Distillation: Towards Efficient and Compact Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Remarkable achievements have been obtained by deep neural networks in the last several years. However, the breakthrough in neural networks accuracy is always accompanied by explosive growth of computation and parameters, which leads to a severe limit...

Neural Granger Causality.

IEEE transactions on pattern analysis and machine intelligence
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsisten...

SANet: A Slice-Aware Network for Pulmonary Nodule Detection.

IEEE transactions on pattern analysis and machine intelligence
Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodule...

Gating Revisited: Deep Multi-Layer RNNs That can be Trained.

IEEE transactions on pattern analysis and machine intelligence
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM [16] and GRU [10] while being more robust against vanishing or exploding gradients. Stacking recurrent units into ...