Hi, I'm Asbjørn

I am a Ph.D. student at the University of Copenhagen and the Pioneer Centre for AI supervised by Mads Nielsen. My research area is self-supervised learning with applications to brain MRI.

I am fortunate to be recipient of the 2023 PhD Fellowship from the Danish Data Science Academy.

March 2025: Proud to be leading the effort to host the first Foundation Model Challenge at MICCAI for Brain MRI! More to come soon at fomo25.github.io.
January 2025: Our work on CLIP applied to Brain MRI to appear at ISBI 2025! This is my first publication as a last author, supervising two talented Master's students, Jakob and Valdemar. Hope to see you in Houston, TX!
August 2024: Our work on AMAES to appear at ADSMI @ MICCAI 2024.
May 2024: Excited to visit Prof. Juan Eugenio Iglesias at the Arthinoula A. Martinos Center, Harvard University.
January 2024: My work on the MDD-UNet was presented at the NLDL 2024 conference in Tromsø, Norway.
June 2023: I got awarded one of the prestigious DDSA PhD Fellowships!

AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation.

Asbjørn Munk*, Jakob Ambsdorf*, Sebastian Llambias, Mads Nielsen
ADSMI @ MICCAI 2024

Efficient pretraining for 3D segmentation models using MAE and augmentation reversal on a large brain MRI dataset. Introduces 🧠BRAINS-45K, the largest pretraining dataset available.
Website · arXiv · GitHub
MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

Asbjørn Munk, Ao Ma, Mads Nielsen
NLDL 2024

Unsupervised domain adaptation framework for U-Nets with theoretical guarantees based on the Margin Disparity Discrepancy.
arXiv · GitHub

Yucca: A modular deep learning framework for 3D medical data

I am contributing to Yucca, a framework that aims to make it effortless for researchers to reach SOTA performance (i.e. nnUNet performance) with minimal code complexity. Focus is on 3D data, in particular CT and MRI.
arXiv · GitHub