Geriatrics

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active researc...

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and g...

Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models.

The health and function of tissue rely on its vasculature network to provide reliable blood perfusio...

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges.

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients w...

Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning.

Early diagnosis of Alzheimer's disease (AD) is a proceeding hot issue along with a sharp upward tren...

NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia.

INTRODUCTION: Using machine learning techniques, we developed a brief questionnaire to aid neurologi...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent...

Prediction of future cognitive impairment among the community elderly: A machine-learning based approach.

The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging,...

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical pop...

Towards end-to-end likelihood-free inference with convolutional neural networks.

Complex simulator-based models with non-standard sampling distributions require sophisticated design...

Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essen...

End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning.

We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) a...

Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly ...

New-Onset Alzheimer's Disease and Normal Subjects 100% Differentiated by P300.

Previous work has suggested that evoked potential analysis might allow the detection of subjects wit...

New Approaches for the Treatment of Delirium: A Case for Robotic Pets.

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline.

Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmenta...

Using machine learning to optimize selection of elderly patients for endovascular thrombectomy.

BACKGROUND: Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic s...

Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors.

The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Ple...

Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of ...

RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment o...

Development of a cardiac-centered frailty ontology.

BACKGROUND: A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to asse...