1 citations · 3 across the 6 of their papers we have counts for
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Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning
Julia Machnio, Sebastian Nørgaard Llambias, Mads Nielsen +1
White matter hyperintensities (WMH) are radiological markers of small vessel disease and neurodegeneration, whose accurate segmentation and spatial localization are crucial for dia…
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…
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…
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
Sebastian Nørgaard Llambias, Mads Nielsen, Mostafa Mehdipour Ghazi
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition para…