Self-Supervised Learning of Accelerometer Data Provides New Insights for Sleep and Its Association with Mortality

Self-Supervised Learning of Accelerometer Data Provides New Insights for Sleep and Its Association with Mortality

Information Science, Artificial Intelligence, Medicine, Psychiatry, Neurology, Life Sciences, Biophysics,
self-supervised learning   accelerometer   sleep   mortality   deep learning   sleep stage classification   multicenter cohort study  

Insights into the Association Between Sleep and Mortality Revealed by Self-supervised Learning of Wrist-worn Accelerometer Data In modern society, sleep is an essential basic activity for life, and its importance is self-evident. Accurately measuring and classifying sleep/wake states and different sleep stages is crucial for diagnosing sleep disord...

Impact of a Deep Learning Sepsis Prediction Model on Quality of Care and Survival

Information Science, Artificial Intelligence, Medicine, Nursing, Emergency Medicine,
deep learning   sepsis   prediction model   quality of care   survival rate   in-hospital mortality   causal impact analysis  

Impact of Deep Learning Sepsis Prediction Model on Nursing Quality and Patient Survival Research Background Sepsis is a systemic inflammatory response caused by infection, affecting approximately 48 million people globally each year, with around 11 million deaths. Due to the heterogeneity of sepsis, early identification often faces significant chal...

Diffusion Model Optimization with Deep Learning

Diffusion Model Optimization with Deep Learning

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
deep learning   diffusion model   neural network   microstructural imaging   self-supervised learning  

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Medicine, Medical Imaging, Life Sciences, Bioengineering,
self-supervised learning   deep learning   convolutional neural network   diffusion tensor imaging   denoising  

Background Introduction Diffusion Tensor Magnetic Resonance Imaging (DTI) is a widely used neuroimaging technique for imaging the microstructure of brain tissues and white matter tracts. However, noise in Diffusion-Weighted Images (DWI) can reduce the accuracy of microstructural parameters derived from DTI data and also necessitate longer acquisiti...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
diffusion tensor imaging   diffusion tractography   tract-specific analysis   deep learning   residual learning   convolutional neural network   data redundancy  

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
deep learning   speech synthesis   neural decoding   brain-computer interface   speech parameters  

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...