AIMC Topic: Humans

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Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences.

Sensors (Basel, Switzerland)
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and...

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data.

International journal of environmental research and public health
BACKGROUND: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the well...

Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks.

Scientific reports
The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network....

Assessment of skin barrier function using skin images with topological data analysis.

NPJ systems biology and applications
Recent developments of molecular biology have revealed diverse mechanisms of skin diseases, and precision medicine considering these mechanisms requires the frequent objective evaluation of skin phenotypes. Transepidermal water loss (TEWL) is commonl...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Physics in medicine and biology
Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. In t...

Detection of transient neurotransmitter response using personalized neural networks.

Physics in medicine and biology
Measurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic positron emission tomography (PET) images typically suffers from limited detection sensitivity and high false positive (FP) rat...

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Physics in medicine and biology
Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised ...

A deep neural network for estimating the bladder boundary using electrical impedance tomography.

Physiological measurement
OBJECTIVE: Accurate bladder size estimation is an important clinical parameter that assists physicians, enabling them to provide better treatment for patients who are suffering from urinary incontinence. Electrical impedance tomography (EIT) is a non...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

Physics in medicine and biology
An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propo...

Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning.

Translational vision science & technology
PURPOSE: To assess the use of deep learning for high-performance image classification of color-coded corneal maps obtained using a Scheimpflug camera.