4 papers
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
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…
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…
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
Jakob Ambsdorf, Asbjørn Munk, Sebastian Llambias +6
With access to large-scale, unlabeled medical datasets, researchers are confronted with two questions: Should they attempt to pretrain a custom foundation model on this medical dat…