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More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation.

Patterns 3:100426 (2022)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Label-efficient algorithms are of central importance for machine learning applications in many medical fields, where obtaining expert annotations is often expensive and time-consuming. Soni et al. show how contrastive learning can help build classifiers for one of the oldest and most revered methods of clinical medicine: auscultation of heart and lung sounds.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Editorial
Korrespondenzautor
e-ISSN 2666-3899
Zeitschrift Patterns
Quellenangaben Band: 3, Heft: 1, Seiten: , Artikelnummer: 100426 Supplement: ,
Verlag Cell Press
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed