AIMC Topic: Young Adult

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Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition.

Sensors (Basel, Switzerland)
In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural ...

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.

NeuroImage. Clinical
Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; th...

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...

Information Theoretic Feature Transformation Learning for Brain Interfaces.

IEEE transactions on bio-medical engineering
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential s...

CytoProcessorTM: A New Cervical Cancer Screening System for Remote Diagnosis.

Acta cytologica
BACKGROUND: Current automated cervical cytology screening systems still heavily depend on manipulation of glass slides. We developed a new system called CytoProcessorTM (DATEXIM, Caen, France), which increases sensitivity and takes advantage of virtu...

Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.

IEEE transactions on cybernetics
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we n...

Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning.

IEEE journal of biomedical and health informatics
Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has resulted in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, w...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Radiology
Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck...

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses.

Food research international (Ottawa, Ont.)
There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described...

Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data.

Journal of affective disorders
OBJECTIVE: Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucial to treatment process. However, due to depression's comorbid nature and the subjectivity in diagnos...