AIMC Topic: Algorithms

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A Deep Learning-Based Approach for High-Throughput Hypocotyl Phenotyping.

Plant physiology
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consumi...

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique chall...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Nature biomedical engineering
Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who e...

Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot.

Sensors (Basel, Switzerland)
The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. Ho...

[Basis and perspectives of artificial intelligence in radiation therapy].

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reas...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Computational and mathematical methods in medicine
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...

A smoothing neural network for minimization l-l in sparse signal reconstruction with measurement noises.

Neural networks : the official journal of the International Neural Network Society
This paper investigates a smoothing neural network (SNN) to solve a robust sparse signal reconstruction in compressed sensing (CS), where the objective function is nonsmooth l-norm and the feasible set satisfies an inequality of l-norm 2≥p...

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

World neurosurgery
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorit...

Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.

European radiology
OBJECTIVE: To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.

The application of artificial neural networks in metabolomics: a historical perspective.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data ...