AIMC Topic: Neural Networks, Computer

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Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.

Scientific reports
Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method fo...

Multiparameter optimisation of a magneto-optical trap using deep learning.

Nature communications
Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions giv...

High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network...

Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy.

IEEE transactions on medical imaging
Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast au...

Use of a convolutional neural network for the classification of microbeads in urban wastewater.

Chemosphere
Scientists are on the lookout for a practical model that can serve as a standard for sorting out, identifying, and characterizing microplastics which are common occurrences in water sources and wastewaters. The microbeads (MBs) used in cosmetics and ...

Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.

Current cardiology reports
PURPOSE OF REVIEW: An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for an...

A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing.

Computational intelligence and neuroscience
Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the require...

Modeling of stem form and volume through machine learning.

Anais da Academia Brasileira de Ciencias
Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the ...

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

Journal of medical Internet research
BACKGROUND: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation ...

Learning SPECT detector angular response function with neural network for accelerating Monte-Carlo simulations.

Physics in medicine and biology
A method to speed up [Formula: see text] simulations of single photon emission computed tomography (SPECT) imaging is proposed. It uses an artificial neural network (ANN) to learn the angular response function (ARF) of a collimator-detector system. T...