AIMC Topic: Deep Learning

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Deep learning versus iterative image reconstruction algorithm for head CT in trauma.

Emergency radiology
PURPOSE: To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.

Computers in biology and medicine
Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains ...

Machine Learning for Auto-Segmentation in Radiotherapy Planning.

Clinical oncology (Royal College of Radiologists (Great Britain))
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability...

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer.

Tomography (Ann Arbor, Mich.)
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study in...

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes.

BMC medical imaging
Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databas...

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation.

Oxidative medicine and cellular longevity
BACKGROUND: Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant c...

Re-evaluating Deep Neural Networks for Phylogeny Estimation: The Issue of Taxon Sampling.

Journal of computational biology : a journal of computational molecular cell biology
Deep neural networks (DNNs) have been recently proposed for quartet tree phylogeny estimation. Here, we present a study evaluating recently trained DNNs in comparison to a collection of standard phylogeny estimation methods on a heterogeneous collect...

Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Journal of computational biology : a journal of computational molecular cell biology
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possi...

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Journal of environmental sciences (China)
Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep lea...

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Journal of hazardous materials
Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consec...