Jin, B;
Barbano, R;
Arridge, S;
Tanno, R;
(2022)
Uncertainty quantification in medical image synthesis.
In: Burgos, N and Svoboda, D, (eds.)
Biomedical Image Synthesis Simulation: Methods and Applications.
Elsevier: Amsterdam, Netherlands.
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Abstract
Machine learning approaches to medical image synthesis have shown outstanding performance, but often do not convey uncertainty information. In this chapter, we survey uncertainty quantification methods in medical image synthesis and advocate the use of uncertainty for improving clinicians’ trust in machine learning solutions. First, we describe basic concepts in uncertainty quantification and discuss its potential benefits in downstream applications. We then review computational strategies that facilitate inference, and identify the main technical and clinical challenges. We provide a first comprehensive review to inform how to quantify, communicate and use uncertainty in medical synthesis applications.
| Type: | Book chapter |
|---|---|
| Title: | Uncertainty quantification in medical image synthesis |
| ISBN-13: | 9780128243497 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.elsevier.com/books/biomedical-image-sy... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | uncertainty quantification; medical image synthesis; deep learning; approximate inference; Bayesian neural networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10129844 |
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