AIMC Topic: Deep Learning

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Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Radiology
Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to corre...

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma.

British journal of cancer
BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of tempora...

Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy.

Medical physics
PURPOSE: Applications of deep learning (DL) are essential to realizing an effective adaptive radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in several critical ART tasks, there remain unsolved challenges to achieve sat...

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy.

IEEE transactions on medical imaging
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine lear...

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning.

IEEE transactions on medical imaging
Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change ...

Vision-Based Driver's Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning.

Sensors (Basel, Switzerland)
Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drive...

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting.

Sensors (Basel, Switzerland)
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to ...

Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection.

Sensors (Basel, Switzerland)
Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abd...

COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks.

Frontiers in public health
The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furtherm...

Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning.

Microbial biotechnology
Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing w...