3 citations · 5 across the 6 of their papers we have counts for
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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…
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Jon Middleton, Marko Bauer, Kaining Sheng +5
The identification and localisation of pathological tissues in medical images continues to command much attention among deep learning practitioners. When trained on abundant datase…