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More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation.
Patterns 3:100426 (2022)
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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Publication type
Article: Journal article
Document type
Editorial
e-ISSN
2666-3899
Journal
Patterns
Quellenangaben
Volume: 3,
Issue: 1,
Article Number: 100426
Publisher
Cell Press
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)