AIMC Topic: Deep Learning

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Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology.

Investigative radiology
Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especially in neuroradiology, to solve a wide range of problems such as segmentation, synthesis of missing sequences, and image quality improvement. Of parti...

Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.

Sensors (Basel, Switzerland)
In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were des...

De novo design of luciferases using deep learning.

Nature
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds, but has been limited by a lack of suitable protein structur...

Computational Thinking Training and Deep Learning Evaluation Model Construction Based on Scratch Modular Programming Course.

Computational intelligence and neuroscience
To improve the algorithmic dimension, critical thinking, and problem-solving ability of computational thinking (CT) in students' programming courses, first, a programming teaching model is constructed based on the scratch modular programming course. ...

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.

Investigative radiology
With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce...

Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone.

European radiology
OBJECTIVES: To determine the optimal inversion time (TI) from Look-Locker scout images using a convolutional neural network (CNN) and to investigate the feasibility of correcting TI using a smartphone.

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis.

Neural networks : the official journal of the International Neural Network Society
Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected dat...

Classifying aneuploidy in genotype intensity data using deep learning.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Aneuploidy is the loss or gain of one or more chromosomes. Although it is a rare phenomenon in liveborn individuals, it is observed in livestock breeding populations. These breeding populations are often routinely genotyped and the genotype intensity...

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen.

Medical physics
BACKGROUND: Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning rec...