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20232026
most citedMDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

3 citations · 5 across the 6 of their papers we have counts for

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eess.IV2025

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning

Stefano Cerri, Asbjørn Munk, Sebastian Nørgaard Llambias +11

We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910…

eess.IV2024★ 1 cited

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

Asbjørn Munk, Jakob Ambsdorf, Sebastian Llambias +1

This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset. We introduce BRAINS-45K, a dataset o…