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