AI-based detection and classification of distal radius fractures using low-effort data labeling: eva...
AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size
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
Objectives
To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm’s performance.
Methods
A total of 15,775 fr...
Alternative Titles
Full title
AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8379111
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8379111
Other Identifiers
ISSN
0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-021-07811-2
