AIMC Topic: Deep Learning

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Deep learning models in medical image analysis.

Journal of oral biosciences
BACKGROUND: Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical stru...

Maskless 2-Dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To develop a deep learning (DL) model to generate synthetic, 2-dimensional subtraction angiograms free of artifacts from native abdominal angiograms.

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

Journal of hazardous materials
The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stabi...

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere ...

Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation.

Computers in biology and medicine
Atopic dermatitis (AD) is a common inflammatory skin disease involving multiple signaling pathways. One of the effective treatment strategies of AD is to develop a new drug capable of regulating the key therapeutic targets. Here we report the combina...

Method for quantifying the reaction degree of slag in alkali-activated cements using deep learning-based electron microscopy image analysis.

Journal of microscopy
In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali-activated cements using neural network based image analysis. The new methodology consists of an image analysis routine...

Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks.

Sensors (Basel, Switzerland)
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution m...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Sensors (Basel, Switzerland)
Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-comple...

Fully automated platelet differential interference contrast image analysis via deep learning.

Scientific reports
Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platele...

Deep learning-based methods for natural hazard named entity recognition.

Scientific reports
Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide...