4.6 Article

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

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Nannapas Banluesombatkul et al.

Summary: This study proposes a novel transfer learning framework MetaSleepLearner based on Meta-Learning (MAML), achieving a range of 5.4% to 17.7% improvement in comparison to the conventional approach. The model interpretation after adaptation to each subject confirms a reasonable learning performance, showcasing potential for human-machine collaboration in sleep stage classification and reducing clinicians' workload.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Computer Science, Information Systems

Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder

Apiwat Ditthapron et al.

IEEE ACCESS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Unequal-training for Deep Face Recognition with Long-tailed Noisy Data

Yaoyao Zhong et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Engineering, Biomedical

ROBUST LEARNING AT NOISY LABELED MEDICAL IMAGES: APPLIED TO SKIN LESION CLASSIFICATION

Cheng Xue et al.

2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) (2019)

Article Computer Science, Information Systems

The National Sleep Research Resource: towards a sleep data commons

Guo-Qiang Zhang et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2018)

Proceedings Paper Computer Science, Artificial Intelligence

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

Yifan Ding et al.

2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) (2018)

Article Computer Science, Information Systems

Data Mining for Patient Friendly Apnea Detection

Stein Kristiansen et al.

IEEE ACCESS (2018)

Article Clinical Neurology

Agreement in the Scoring of Respiratory Events Among International Sleep Centers for Home Sleep Testing

Ulysses J. Magalang et al.

JOURNAL OF CLINICAL SLEEP MEDICINE (2016)

Article Automation & Control Systems

Multi-Step Ahead Predictions for Critical Levels in Physiological Time Series

Hisham ElMoaqet et al.

IEEE TRANSACTIONS ON CYBERNETICS (2016)

Article Biophysics

Evaluating predictions of critical oxygen desaturation events

Hisham ElMoaqet et al.

PHYSIOLOGICAL MEASUREMENT (2014)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Clinical Neurology

The New AASM Criteria for Scoring Hypopneas: Impact on the Apnea Hypopnea Index

Warren R. Ruehland et al.

SLEEP (2009)