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Radiology
Article . 2024 . Peer-reviewed
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Radiology
Article . 2024
Data sources: Europe PubMed Central
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Weakly Supervised Deep Learning in Radiology

descriptionPublicationkeyboard_double_arrow_right Article 01 Jul 2024 English Publisher:Radiological Society of North America (RSNA)Journal:Radiology, volume 312 (issn: 0033-8419, eissn: 1527-1315, Copyright policy )
Authors: Leo Misera; Gustav Müller-Franzes; Daniel Truhn; Jakob Nikolas Kather;

doi: 10.1148/radiol.232085

pmid: 39041937

Weakly Supervised Deep Learning in Radiology

Abstract

This review explores the transition of deep learning in radiology from laborious fully supervised methods to more scalable weakly supervised methods, emphasizing the potential and challenges for future research.

Related Organizations
Keywords

Deep Learning, Image Interpretation, Computer-Assisted, Humans, Supervised Machine Learning, Radiology

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    popularity
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    influence
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
30
Top 10%
Top 10%
Top 1%
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