Opening the black box of machine learning in radiology: can the proximity of annotated cases be a wa...
Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
About this item
Full title
Author / Creator
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
Subjects
More information
Scope and Contents
Contents
Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as
black boxes
. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiolo...
Alternative Titles
Full title
Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_838a5dc06497454cbe95192ad31e26e8
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_838a5dc06497454cbe95192ad31e26e8
Other Identifiers
ISSN
2509-9280
E-ISSN
2509-9280
DOI
10.1186/s41747-020-00159-0
