Is Uncertainty Quantification a Viable Alternative to Learned Deferral?

dc.contributor.author	Baumgartner, Christian F.
dc.date.accessioned	2025-10-24T08:56:17Z
dc.date.available	2025-10-24T08:56:17Z
dc.date.issued	2025-09-29
dc.identifier.isbn	978-3-032-06592-6
dc.identifier.isbn	978-3-032-06593-3
dc.identifier.uri	http://hdl.handle.net/10900/171550
dc.language.iso	en	de_DE
dc.publisher	Springer Nature	de_DE
dc.relation.uri	https://doi.org/10.1007/978-3-032-06593-3_4	de_DE
dc.subject.ddc	004	de_DE
dc.title	Is Uncertainty Quantification a Viable Alternative to Learned Deferral?	de_DE
dc.type	Article	de_DE
utue.publikation.seiten	34-44	de_DE
utue.personen.roh	Wundram, Anna M.
utue.personen.roh	Baumgartner, Christian F.
dcterms.isPartOf.ZSTitelID	Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE 2025)	de_DE
dcterms.isPartOf.ZS-Volume	vol 16166	de_DE