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Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7eab8484eb524e2c98222cbff9e32e0e

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

About this item

Full title

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Publisher

John Wiley & Sons, Inc

Journal title

Electronics Letters, 2024-07, Vol.60 (14), p.n/a

Language

English

Formats

Publication information

Publisher

John Wiley & Sons, Inc

More information

Scope and Contents

Contents

Retinopathy of prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early‐stage ROP diagnosis based on limited‐labelled images in an imbalanced dataset. To addres...

Alternative Titles

Full title

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_7eab8484eb524e2c98222cbff9e32e0e

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7eab8484eb524e2c98222cbff9e32e0e

Other Identifiers

ISSN

0013-5194

E-ISSN

1350-911X

DOI

10.1049/ell2.13267

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