Results 111 to 120 of about 115,093 (312)

Contrastive Learning for Lifted Networks

open access: yes, 2019
In this work we address supervised learning of neural networks via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel hardware and assign energy models to discriminatively trained neural ...
Estellers, Virginia, Zach, Christopher
core  

Lessons Learned: Quality Analysis of Optical Coherence Tomography in Neuromyelitis Optica

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Optical coherence tomography (OCT)‐derived retina measurements are markers for neuroaxonal visual pathway status. High‐quality OCT scans are essential for reliable measurements, but their acquisition is particularly challenging in eyes with severe visual impairment, as often observed in neuromyelitis optica spectrum disorders ...
Hadi Salih   +40 more
wiley   +1 more source

Towards Precise and Robust Hippocampus Segmentation using Self-Supervised Contrastive Learning

open access: hybrid, 2022
Kassymzhomart Kunanbayev   +3 more
openalex   +1 more source

Portable Low‐Field Magnetic Resonance Imaging in People With Human Immunodeficiency Virus

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The aging population of people with HIV (PWH) raises heightened concerns regarding accelerated aging and dementia. Portable, low‐field MRI (LF‐MRI) is an innovative technology that could enhance access and facilitate routine monitoring of PWH.
Annabel Sorby‐Adams   +14 more
wiley   +1 more source

Domain Specific Placebo Response in the Modified Friedreich's Ataxia Rating Scale

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT The placebo response in clinical trials in ataxias complicates outcome interpretation and potentially obscures genuine treatment effects. We analyzed placebo group data from past trials in Friedreich Ataxia and observed notable responses in appendicular items, in contrast to minimal changes in axial function, as measured by respective ...
Christian Rummey   +2 more
wiley   +1 more source

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein   +7 more
wiley   +1 more source

Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas   +7 more
wiley   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu   +11 more
wiley   +1 more source

A Good View for Graph Contrastive Learning

open access: yesEntropy
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within
Xueyuan Chen, Shangzhe Li
doaj   +1 more source

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