Results 11 to 20 of about 184,556 (342)
Comparison between 3D SPACE FLAIR and 3D TSE FLAIR in Menière’s disease [PDF]
Neuroradiology, 2022 Heavily T2-weighted 3D FLAIR (hT2w-3D-FLAIR) sequence with constant flip angle (CFA) has been reported as being more sensitive to low concentrations of gadolinium (Gd) enabling endolymphatic hydrops (EH) visualization.
A. Bernaerts +10 more
semanticscholar +4 more sources
T1w/FLAIR ratio standardization as a myelin marker in MS patients
NeuroImage: Clinical, 2022 Introduction: Calculation of a T1w/T2w ratio was introduced as a proxy for myelin integrity in the brain of multiple sclerosis (MS) patients. Since nowadays 3D FLAIR is commonly used for lesion detection instead of T2w images, we introduce a T1w/FLAIR ...
S. Cappelle +6 more
doaj +2 more sources
Scientific Reports, 2021 From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in ...
Leehi Joo +6 more
doaj +2 more sources
Frontiers in Oncology, 2021 PurposeTo demonstrate that quantitative multicomponent T2 relaxation can be more sensitive than conventional FLAIR imaging for detecting cerebral tissue abnormalities.MethodsSix patients affected by lower-grade non-enhancing gliomas underwent T2 ...
Pietro Bontempi +5 more
doaj +2 more sources
A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision [PDF]
Medical Image Anal., 2023 Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities.
Julio Silva-Rodríguez +4 more
semanticscholar +1 more source
FLAIR: Federated Learning Annotated Image Repository [PDF]
Neural Information Processing Systems, 2022 Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices.
Congzheng Song +2 more
semanticscholar +1 more source
DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images [PDF]
International Journal of Computer Assisted Radiology and Surgery, 2020 Purpose Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine.
Ramy A. Zeineldin +4 more
semanticscholar +1 more source
FLAIR #1: semantic segmentation and domain adaptation dataset [PDF]
arXiv.org, 2022 The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps.
A. Garioud +4 more
semanticscholar +1 more source
FLAIR: Defense against Model Poisoning Attack in Federated Learning
ACM Asia Conference on Computer and Communications Security, 2023 Federated learning—multi-party, distributed learning in a decentralized environment—is vulnerable to model poisoning attacks, more so than centralized learning. This is because malicious clients can collude and send in carefully tailored model updates to
Atul Sharma +5 more
semanticscholar +1 more source
Cancers, 2023 Simple Summary The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted diffuse gliomas with a high specificity and modest sensitivity.
S. Kihira +14 more
semanticscholar +1 more source