More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 35079721.
- Also identified by DOI 10.1016/j.patter.2021.100426 and PMC identifier 8767290.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.