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Revisiting Hidden Representations in Transfer Learning for Medical Imaging

Revisiting Hidden Representations in Transfer Learning for Medical Imaging

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2777530372

Revisiting Hidden Representations in Transfer Learning for Medical Imaging

About this item

Full title

Revisiting Hidden Representations in Transfer Learning for Medical Imaging

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2023-12

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

While a key component to the success of deep learning is the availability of massive amounts of training data, medical image datasets are often limited in diversity and size. Transfer learning has the potential to bridge the gap between related yet different domains. For medical applications, however, it remains unclear whether it is more beneficia...

Alternative Titles

Full title

Revisiting Hidden Representations in Transfer Learning for Medical Imaging

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2777530372

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2777530372

Other Identifiers

E-ISSN

2331-8422

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