Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images

Author:

Dawood TareenORCID,Chen ChenORCID,Sidhu Baldeep S.ORCID,Ruijsink BramORCID,Gould Justin,Porter Bradley,Elliott Mark K.ORCID,Mehta VishalORCID,Rinaldi Christopher A.,Puyol-Antón EstherORCID,Razavi Reza,King Andrew P.ORCID

Funder

Wellcome Trust

EPSRC Centre for Doctoral Training in Smart Medical Imaging

NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research

NIHR Biomedical Research Unit

Engineering and Physical Sciences Research Council

National Institute for Health and Care Research

King's College London

Guy's and St Thomas' NHS Foundation Trust

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference38 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Inf. Fusion,2021

2. Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020);Alizadehsani;Ann. Oper. Res.,2021

3. Pitfalls of in-domain uncertainty estimation and ensembling in deep learning;Ashukha,2020

4. Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural network;Ayhan,2018

5. Deep ROC analysis and AUC as balanced average accuracy to improve model selection, understanding and interpretation;Carrington,2021

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