A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms
type: Item
instance of: scholarly article
title: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms
zbMATH Open document ID: 1473.92023
author: Wenqi Ao
author: Wenbin Li
author: Jianliang Qian
publication date: 2021-08-05T00:00:00Z
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 65R32
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7379125
zbMATH Keywords: data and knowledge driven
zbMATH Keywords: convolutional neural networks
zbMATH Keywords: iterative algorithms
zbMATH Keywords: integral equations
describes a project that uses: ImageNet
describes a project that uses: AlexNet
describes a project that uses: Adam
describes a project that uses: U-Net
MaRDI profile type: Publication
full work available at URL: jiip-2020-0056
OpenAlex ID: W3144480604
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cites work: An inversion formula for the attenuated \(X\)-ray transformation
cites work: Edge-preserving and scale-dependent properties of total variation regularization
cites work: A unified primal-dual algorithm framework based on Bregman iteration
DOI: 10.1515/JIIP-2020-0056
published in: Journal of Inverse and Ill-posed Problems
description: scientific article; zbMATH DE number 7379125