How to deal with Uncertainty in Machine Learning for Medical Imaging?

Abstract

Recently, machine learning is massively on the rise in medical applications providing the ability to predict diseases, plan treatment and monitor progress. Still, the use in a clinical context of this technology is rather rare, mostly due to the missing trust of clinicians. In this position paper, we aim to show how uncertainty is introduced in the machine learning process when applying it to medical imaging at multiple points and how this influences the decision-making process of clinicians in machine learning approaches. Based on this knowledge, we aim to refine the guidelines for trust in visual analytics to assist clinicians in using and understanding systems that are based on machine learning.