My PhD focuses on Continual Learning and Out-of-distribution detection for magnetic resonance images (MRIs) and computer tomography (CT) scans. Due to differences in the acquisition process across -and even within- medical institutions, this data often suffers from domain shift. Deep Learning models which are not trained specifically to deal with multi-domain data behave unreliably when applied to out-of-distribution images. My goal is to facilitate the use of Deep learning models for both segmentation and classification/detection in clinical multi-institutional settings.

I am mainly involved in the RACOON and EVA-KI projects. I am also a member of the MICCAI Student Board. You can follow me on Twitter at @camgbus and see my publication list in Google Scholar. My work was recently featured in Computer Vision News.

If you’re interested in these topics and have experience working with PyTorch, please write me an email to inquire about current (Fortgeschrittenes) Visual Computing Praktikum (6 CP) and Master Thesis topics.