Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as healthcare. We propose an effective new approach for learning under extreme label noise for medical applications like s...

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Bibliographic Details
Main Authors: Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen, Vera Goebel, Mohan Kankanhalli
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Biomedical informatics
supervised learning
sleep apnea
machine learning
label noise
Online Access:https://ieeexplore.ieee.org/document/9381860/

Internet

https://ieeexplore.ieee.org/document/9381860/

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