AIMC Topic: Deep Learning

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Deep learning methodology for predicting time history of head angular kinematics from simulated crash videos.

Scientific reports
Head kinematics information is important as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. This paper evaluates the deep learning approach in predicting ...

Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph.

BMC neurology
BACKGROUND: Deep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is highly ...

A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition.

Computational intelligence and neuroscience
Human action recognition is an important field in computer vision that has attracted remarkable attention from researchers. This survey aims to provide a comprehensive overview of recent human action recognition approaches based on deep learning usin...

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

The Science of the total environment
This study proposes a new model for the spatiotemporal prediction of PM concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU)...

Deep learning, reinforcement learning, and world models.

Neural networks : the official journal of the International Neural Network Society
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuros...

Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge.

Computer methods and programs in biomedicine
BACKGROUND: Due to the advancement of medical imaging and computer technology, machine intelligence to analyze clinical image data increases the probability of disease prevention and successful treatment. When diagnosing and detecting heart disease, ...

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to ...

A non-invasive approach to monitor anemia during long-duration spaceflight with retinal fundus images and deep learning.

Life sciences in space research
During spaceflight, astronauts can experience significantly higher levels of hemolysis. With future space missions exposing astronauts to longer periods of microgravity, such as missions to Mars, there will be a need to better understand this phenome...

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data.

BMC bioinformatics
BACKGROUND: Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identific...

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.

BMC bioinformatics
BACKGROUND: Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality o...