AIMC Topic: Neural Networks, Computer

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Balancing Biases and Preserving Privacy on Balanced Faces in the Wild.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Faces in the Wild (BFW) dataset. This dataset allows for the characterizat...

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Computer methods in biomechanics and biomedical engineering
Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this ...

Distribution Patterns of Subgroups of Inhibitory Neurons Divided by Calbindin 1.

Molecular neurobiology
The inhibitory neurons in the brain play an essential role in neural network firing patterns by releasing γ-aminobutyric acid (GABA) as the neurotransmitter. In the mouse brain, based on the protein molecular markers, inhibitory neurons are usually t...

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Computational biology and chemistry
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-c...

Multitasking via baseline control in recurrent neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefit...

Experimental validation of the free-energy principle with in vitro neural networks.

Nature communications
Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, w...

MHL-Net: A Multistage Hierarchical Learning Network for Head and Neck Multiorgan Segmentation.

IEEE journal of biomedical and health informatics
Accurate segmentation of head and neck organs at risk is crucial in radiotherapy. However, the existing methods suffer from incomplete feature mining, insufficient information utilization, and difficulty in simultaneously improving the performance of...

Remote Blood Oxygen Estimation From Videos Using Neural Networks.

IEEE journal of biomedical and health informatics
Peripheral blood oxygen saturation (SpO ) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO before any ...

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

IEEE journal of biomedical and health informatics
Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in t...

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel relations that are known to be beneficial for EEG-based classification...