AIMC Topic: Neural Networks, Computer

Clear Filters Showing 26971 to 26980 of 31376 articles

Ultra-Sparse-View Cone-Beam CT Reconstruction-Based Strictly Structure-Preserved Deep Neural Network in Image-Guided Radiation Therapy.

IEEE transactions on medical imaging
Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT...

GDP-Net: Global Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal.

IEEE transactions on medical imaging
In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on...

TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers.

IEEE journal of biomedical and health informatics
Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus ...

FeaInfNet: Diagnosis of Medical Images With Feature-Driven Inference and Visual Explanations.

IEEE journal of biomedical and health informatics
Interpretable deep-learning models have received widespread attention in the field of image recognition. However, owing to the coexistence of medical-image categories and the challenge of identifying subtle decision-making regions, many proposed inte...

Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography.

IEEE journal of biomedical and health informatics
The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables...

CT-DCENet: Deep EEG Denoising via CNN-Transformer-Based Dual-Stage Collaborative Ensemble Learning.

IEEE journal of biomedical and health informatics
Electroencephalogram (EEG) artifact removal has been investigated for decades with the goal of reconstructing the clean signals for the subsequent EEG analysis. However, existing denoising methods still have limited capabilities to handle the highly ...

Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution.

IEEE transactions on neural networks and learning systems
Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use ...

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...

Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.

IEEE journal of biomedical and health informatics
Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain response...

Automated Ensemble Multimodal Machine Learning for Healthcare.

IEEE journal of biomedical and health informatics
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. Thi...