AIMC Topic: Deep Learning

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Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions.

Cell host & microbe
Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from host-microbe interactions. Here, we present machine learning and bioinformatics methods to leverage the evolutionary information present in glycans to gain insig...

A deep learning approach for sepsis monitoring via severity score estimation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personne...

Deep learning for digitizing highly noisy paper-based ECG records.

Computers in biology and medicine
Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the ...

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

Computers in biology and medicine
With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using C...

Deep learning reconstruction of equilibrium phase CT images in obese patients.

European journal of radiology
PURPOSE: To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients.

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

IEEE transactions on medical imaging
Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bay...

Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.

IEEE transactions on medical imaging
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from mu...

Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal.

IEEE transactions on medical imaging
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm...

Deep-Learning Image Reconstruction for Real-Time Photoacoustic System.

IEEE transactions on medical imaging
Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an inverse probl...

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Sensors (Basel, Switzerland)
Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measur...