AIMC Topic: Deep Learning

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Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data.

European journal of sport science
This study examined a series of machine learning models, evaluating their effectiveness in assessing children's energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also de...

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multipara...

Distortion correction of single-shot EPI enabled by deep-learning.

NeuroImage
PURPOSE: A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network.

Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.

Accident; analysis and prevention
Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicl...

Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy.

Medical physics
PURPOSE: High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implan...

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.

Viruses
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity si...

A deep learning model for detecting mental illness from user content on social media.

Scientific reports
Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from m...

Deep learning-based classification of the mouse estrous cycle stages.

Scientific reports
There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from vaginal smear cytology. However, the determination of estrous stages requires extensive training, tak...

Semantic segmentation to identify bladder layers from H&E Images.

Diagnostic pathology
BACKGROUND: Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which recognizes urothelium, lamina propria, muscularis propria, and muscularis...