Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Purpose
We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.
Methods
Eighty simultaneous [
18
F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa...
Alternative Titles
Full title
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7680289
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7680289
Other Identifiers
ISSN
1619-7070
E-ISSN
1619-7089
DOI
10.1007/s00259-020-04897-6
