Matek C (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 3
Article Number: 100426
Journal Issue: 1
DOI: 10.1016/j.patter.2021.100426
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.
APA:
Matek, C. (2022). More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation. Patterns, 3(1). https://dx.doi.org/10.1016/j.patter.2021.100426
MLA:
Matek, Christian. "More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation." Patterns 3.1 (2022).
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