Hi, I'm Asbjørn Munk
I am a Ph.D. student at the University of Copenhagen and the Pioneer Centre for AI supervised by Mads Nielsen. I am fortunate to be recipient of the 2023 PhD Fellowship from the Danish Data Science Academy. My research area is self-supervised learning for medical imaging with a focus on brain MRI (and occasionally fetal ultrasound).
Currently, I am visiting Juan Eugenio Iglesias' group at the Martinos Center, (Harvard & MGH).
asmu@di.ku.dk · twitter · linkedin · google scholar
News
- July 2025: I'm in Boston! 🇺🇸 For the next six months I will be at Prof. Juan Eugenio Iglesias' lab at the Martinos Center, Harvard Medical School & Massachusetts General Hospital. If you are in the area, feel free to reach out!
- June 2025: Our paper on UltraDINO got accepted to MICCAI 2025! Paper is available on arXiv. Hope to see you in South Korea! 🇰🇷
- June 2025: In conjunction with the FOMO25 challenge we have released FOMO60k, a large-scale pretraining dataset for Brain MRI. It's available via Hugging Face 🤗, details available in this preprint.
- March 2025: Proud to be leading the effort to host the first Foundation Model Challenge at MICCAI for Brain MRI! Read more and sign up 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 in Morocco! 🇲🇦
- May 2024: Excited to visit Prof. Juan Eugenio Iglesias at the Martinos Center, Harvard / MGH.
- 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!
Selected Publications
Full list of publications can be found on my google scholar. * denotes equal contribution.
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General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
Asbjørn Munk*, Jakob Ambsdorf*, Sebastian Llambias, Anders Nymark Christensen, Kamil Mikolaj, Randall Balestriero, Martin Tolsgaard, Aasa Feragen, Mads Nielsen
MICCAI 2025Introduces UltraDINO, a foundation model for fetal ultrasound trained on 2M images based on iBOT and DINOv2, achieving state-of-the-art performance on fetal ultrasound classification and segmentation tasks.
arXiv · GitHub -
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation.
Asbjørn Munk*, Jakob Ambsdorf*, Sebastian Llambias, Mads Nielsen
ADSMI @ MICCAI 2024Efficient 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 2024Unsupervised domain adaptation framework for U-Nets with theoretical guarantees based on the Margin Disparity Discrepancy.
arXiv · GitHub
Other projects
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FOMO25: Foundation Model Challenge for Brain MRI at MICCAI
Leading the effort to host the first Foundation Model Challenge at MICCAI for Brain MRI. Read more and signup at fomo25.github.io.
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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