AIMC Topic: Neural Networks, Computer

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Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks for image dehazing.

Neural networks : the official journal of the International Neural Network Society
Recently, Unsupervised algorithms has achieved remarkable performance in image dehazing. However, the CycleGAN framework can lead to confusion in generator learning due to inconsistent data distributions, and the DisentGAN framework lacks effective c...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

Neural networks : the official journal of the International Neural Network Society
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) a...

Unsupervised model adaptation for source-free segmentation of medical images.

Medical image analysis
The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when task...

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular for...

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Methods (San Diego, Calif.)
Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential t...

Human Action Recognition and Note Recognition: A Deep Learning Approach Using STA-GCN.

Sensors (Basel, Switzerland)
Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being play...

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks.

Sensors (Basel, Switzerland)
The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated pre...

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Molecules (Basel, Switzerland)
Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model t...

A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction.

International journal of neural systems
Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The...

Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

International journal of neural systems
Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases ...