Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap be...
Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap between thyroid cancer histopathology datasets
About this item
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Publisher
Public Library of Science (PLoS)
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Language
English
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Publisher
Public Library of Science (PLoS)
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Scope and Contents
Contents
Deep learning techniques are increasingly being used to classify medical imaging data with high accuracy. Despite this, due to often limited training data, these models can lack sufficient generalizability to predict unseen test data, produced in different domains, with comparable performance. This study focuses on thyroid histopathology image clas...
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Full title
Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap between thyroid cancer histopathology datasets
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TN_cdi_doaj_primary_oai_doaj_org_article_d8c135489d604465be45524cf6ef7328
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d8c135489d604465be45524cf6ef7328
Other Identifiers
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0310417
