Robustness, Safety and Uncertainty

Segmentation and variance map obtained using the PHISeg method

In medical image analysis, confidently predicting something false can have devastating consequences. Apart from achieving high predictive accuracy, one needs to establish the circumstances under which algorithmic predictions generalize, or give appropriate error bounds. This is highly relevant to patient safety and the regulation of machine learning based medical software. The goal in this branch, is develop deep learning methods founded in probability theory that can be used to estimate uncertainty stemming from sources such as the image reconstruction, or inherent limitations of an imaging modality.

Selected publications

Machine Learning in Medical Image Analysis
Machine Learning in Medical Image Analysis
Bridging the gap between AI and clinical practice