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Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI

; ; ;

In
SIAM ASA journal on uncertainty quantification 13(1), Seiten/Artikel-Nr.:90-113

ImpressumPhiladelphia, Pa. : SIAM

ISSN2166-2525

Online
DOI: 10.1137/23M161433X

DOI: 10.18154/RWTH-2025-04436
URL: http://publications.rwth-aachen.de/record/1010924/files/1010924.pdf

Einrichtungen

  1. Lehrstuhl für Mathematics for Uncertainty Quantification (118110)
  2. Fachgruppe Mathematik (110000)


Thematische Einordnung (Klassifikation)
DDC: 510

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85215551527
WOS Core Collection: WOS:001450056300004

Interne Identnummern
RWTH-2025-04436
Datensatz-ID: 1010924

Beteiligte Länder
Germany, Saudi Arabia

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Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2025-05-06, last modified 2025-07-31
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