scholarly journals Single-Pixel Image Reconstruction from Experimental Data Using Neural Networks

2021 ◽  
Author(s):  
Antonio Lorente Mur ◽  
Nicolas Ducros ◽  
Françoise Peyrin ◽  
Pierre Leclerc
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Image Reconstruction ◽  
Pixel Image ◽  
Single Pixel

scholarly journals Identifying nonclassicality from experimental data using artificial neural networks

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Valentin Gebhart ◽  
Martin Bohmann ◽  
Karsten Weiher ◽  
Nicola Biagi ◽  
Alessandro Zavatta ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Accurate Modeling of Complex Antitoxin Effect of Quercetin Based on Neural Networks

2019 ◽  
Vol 29 (01) ◽  
pp. 1950013 ◽  
Author(s):  
Changju Yang ◽  
Entaz Bahar ◽  
Hyonok Yoon ◽  
Hyongsuk Kim
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Secondary Metabolites ◽  
Hela Cell ◽  
Protective Effect ◽  
Nonlinear Modeling ◽  
Inflammatory Activity ◽  
High Complexity ◽  
Anti Oxidant

A nonlinear modeling of the protective effect of Quercetin (QCT) against various Mycotoxins (MTXs) has a high complexity and is conducted using artificial neural networks (ANNs). QCT is known to possess strong anti-oxidant, anti-inflammatory activity that can prevent many diseases. MTXs are highly toxic secondary metabolites that are capable of causing disease and death in humans and animals. The protective model of QCT against various MTXs (Citrinin, Patulin and Zearalenol) on HeLa cell is built accurately via learning of sparsely measured experimental data by the ANNs. It has shown that the neuro-model can predict the nonlinear protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage of inhibition of MTXs.

Active Microwave Imaging II: 3-D System Prototype and Image Reconstruction From Experimental Data

2008 ◽  
Vol 56 (4) ◽  
pp. 991-1000 ◽  
Author(s):  
Chun Yu ◽  
Mengqing Yuan ◽  
J. Stang ◽  
E. Bresslour ◽  
R.T. George ◽  
...  
Keyword(s):  
Experimental Data ◽  
Image Reconstruction ◽  
Microwave Imaging ◽  
System Prototype

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and predictive neural networks modeling

NanoImpact ◽  
2021 ◽  
pp. 100317
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Michael Syzochenko ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Metal Oxide ◽  
Meta Analysis ◽  
Metal Oxide Nanoparticles ◽  
Oxide Nanoparticles ◽  
Zeta Potentials

scholarly journals A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

2018 ◽  
Vol 37 (2) ◽  
pp. 491-503 ◽  
Author(s):  
Jo Schlemper ◽  
Jose Caballero ◽  
Joseph V. Hajnal ◽  
Anthony N. Price ◽  
Daniel Rueckert
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Convolutional Neural Networks ◽  
Mr Image ◽  
Dynamic Mr ◽  
Mr Image Reconstruction

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics

2020 ◽  
Vol 26 (3) ◽  
pp. 403-412 ◽  
Author(s):  
Pavel Potocek ◽  
Patrick Trampert ◽  
Maurice Peemen ◽  
Remco Schoenmakers ◽  
Tim Dahmen
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Real Life ◽  
Large Field ◽  
Acquisition Time ◽  
Scanning Electron Microscopy Data ◽  
Scanning Electron

AbstractWith the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

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