Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) v...
Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study
About this item
Full title
Author / Creator
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
Subjects
More information
Scope and Contents
Contents
Purpose
To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging reporting and data system (LI-RADS) (version 2014).
Methods
A pre-existing dataset comprising 314 hepatic observations (163 CT, 151 MRI) with corresponding diameters and LI-RADS categories (LR-1–...
Alternative Titles
Full title
Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6946904
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6946904
Other Identifiers
ISSN
2366-004X
E-ISSN
2366-0058
DOI
10.1007/s00261-019-02306-7
