AIMC Topic: Deep Learning

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Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

Abdominal radiology (New York)
PURPOSE: In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy f...

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.

A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The morphology of the human metaphase II (MII) oocyte is an essential indicator of the embryo's potential for developing into a healthy baby in the Intra-Cytoplasmic Sperm Injection (ICSI) process. In this case, characterist...

TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Journal of chemical information and modeling
Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques...

Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification.

Scientific reports
In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where ...

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Genome biology
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present Methyla...

Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

Biomedical journal
BACKGROUND: Early detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning sc...

Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic.

Emergency radiology
PURPOSE: The purpose of this study was to develop an automated process to analyze multimedia content on Twitter during the COVID-19 outbreak and classify content for radiological significance using deep learning (DL).

A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis.

Biotechnology journal
Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective metho...

The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolution; international journal of organic evolution
Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signal...