Results 31 to 40 of about 33,587 (210)

Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

open access: yesRemote Sensing, 2021
Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task ...
Shuyang Wang   +4 more
doaj   +1 more source

Attitudes of cancer patients toward ctDNA‐based liquid biopsy for follow‐up

open access: yesMolecular Oncology, EarlyView.
This mixed‐methods study explored cancer patients' experiences, knowledge, and preferences regarding ctDNA‐based liquid biopsy for follow‐up. Awareness of ctDNA was low and recall of previous liquid biopsy was imperfect, yet acceptance was high.
Siddharth Chhajlani   +7 more
wiley   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif   +17 more
wiley   +1 more source

Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction

open access: yesRemote Sensing, 2018
Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications.
Sanjeevan Shrestha, Leonardo Vanneschi
doaj   +1 more source

Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

open access: yesApplied Sciences, 2020
Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As
Xinying Xu   +5 more
doaj   +1 more source

Nationwide Survey of Association Between Fever and Epileptic Seizure in CDKL5 Deficiency Disorder Revealed Therapeutic Implications

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective CDKL5 deficiency disorder (CDD) is a rare, severe developmental and epileptic encephalopathy. There is a pressing need to develop effective and sustainable therapeutic strategies. We aimed to investigate the causal association between febrile episodes and epileptic seizures for therapeutic implications in CDD patients.
Siyi Wang   +13 more
wiley   +1 more source

On the continuity of the vector valued and set valued conditional expectations

open access: yesInternational Journal of Mathematics and Mathematical Sciences, 1989
In this paper we study the dependence of the vector valued conditional expectation (for both single valued and set valued random variables), on the σ–field and random variable that determine it. So we prove that it is continuous for the L1(X) convergence
Nikolaos S. Papageorgiou
doaj   +1 more source

Subjective Cognitive Concerns and Cognitive Trajectories in Parkinson's Disease: Biomarker Impact

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the relationship of subjective cognitive concerns (SCC) with biomarkers of Alzheimer's disease (AD), neurodegeneration, and Parkinson's disease (PD) and domain‐specific cognitive trajectories among cognitively unimpaired persons with de novo PD. Method Cognitively unimpaired participants with SCC (n = 294) and without SCC (
Francesca V. Lopez   +5 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +3 more
wiley   +1 more source

Higher Order Support Vector Random Fields for Hyperspectral Image Classification

open access: yesISPRS International Journal of Geo-Information, 2018
This paper addresses the problem of contextual hyperspectral image (HSI) classification. A novel conditional random fields (CRFs) model, known as higher order support vector random fields (HSVRFs), is proposed for HSI classification.
Junli Yang   +3 more
doaj   +1 more source

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