Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

Tareen Dawood*, Chen Chen, Robin Andlauer, Baldeep S. Sidhu, Bram Ruijsink, Justin Gould, Bradley Porter, Mark Elliott, Vishal Mehta, C. Aldo Rinaldi, Esther Puyol-Antón, Reza Razavi, Andrew P. King

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

3 Citations (Scopus)


Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large sets of data but they often lack comprehensive interpretability methods, preventing clinical trust in predictive outcomes. Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. However, relatively little attention has been paid to how to include such requirements into the training of the model. In this paper we: (i) quantify the data (aleatoric) and model (epistemic) uncertainty of a DL model for Cardiac Resynchronisation Therapy response prediction from cardiac magnetic resonance images, and (ii) propose and perform a preliminary investigation of an uncertainty-aware loss function that can be used to retrain an existing DL image-based classification model to encourage confidence in correct predictions and reduce confidence in incorrect predictions. Our initial results are promising, showing a significant increase in the (epistemic) confidence of true positive predictions, with some evidence of a reduction in false negative confidence.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge - 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers
EditorsEsther Puyol Antón, Alistair Young, Avan Suinesiaputra, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Oscar Camara, Karim Lekadir
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030937218
Publication statusPublished - 2022
Event12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021 - Strasbourg, France
Duration: 27 Sept 202127 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021


  • Awareness
  • Cardiac resynchronisation therapy
  • Reliability
  • Trust
  • Uncertainty


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